Quantum Articles 2022

Securing Logistics in the Quantum Era

On December 21, 2022, the European Union Agency for Cybersecurity (ENISA) issued an updated supply chain threat landscape report that underscored the urgency of preparing for the cybersecurity implications of quantum computing. This announcement came at a pivotal moment, only months after the U.S. National Institute of Standards and Technology (NIST) finalized its first set of post-quantum cryptography (PQC) algorithms intended to replace vulnerable standards such as RSA and ECC.

For the logistics sector—where secure communication, shipment data integrity, and trust across international networks are fundamental—the December 2022 developments marked a turning point. For the first time, multiple national and regional agencies aligned their guidance on PQC in the context of supply chain resilience, elevating the issue from a theoretical concern to an operational priority.

Why Post-Quantum Security Matters in Logistics

Modern logistics ecosystems are built on digital trust. Every movement of a container, shipment validation, customs clearance, and warehouse transaction depends on encryption. Today’s widely used cryptographic protocols—particularly RSA and ECC—are at risk of being rendered obsolete once quantum computers mature enough to execute Shor’s algorithm, which can factor large primes and break these systems.

This creates vulnerabilities across:

• Smart ports, where IoT sensors track cargo.

• Autonomous vehicle fleets, which exchange data in real time.

• Blockchain-based supply chain platforms, where contract integrity is paramount.

• Cross-border customs systems, which require secure digital documentation.

Logistics networks are highly interconnected and global, often involving dozens of third-party vendors. A single cryptographic failure could cascade into disruptions that affect entire trade lanes. Therefore, PQC adoption is no longer optional—it is a strategic necessity.

December 2022: Global Coordination Ramps Up

ENISA’s December 21 update built directly on NIST’s July 2022 announcement of four PQC algorithms chosen for standardization:

• CRYSTALS-Kyber (encryption/key establishment)

• CRYSTALS-Dilithium (digital signatures)

• FALCON (digital signatures)

• SPHINCS+ (hash-based signatures)

December 2022 was significant because it was the first month when multiple international agencies synchronized their approaches. The U.S. Department of Homeland Security (DHS) issued its own roadmap in parallel, with specific recommendations for logistics firms, port operators, and freight forwarders. DHS urged supply chain actors to begin inventorying vulnerable systems and testing migration plans.

Enterprise Adoption and Industry Reaction

Several logistics and technology leaders responded quickly to December’s announcements:

• Maersk began evaluating PQC-ready encryption for its terminal operating systems and blockchain-based TradeLens platform.

• DHL Supply Chain announced internal readiness assessments to prepare for PQC rollouts in its cross-border customs and security workflows.

• IBM updated its PQC toolkit to support Kyber and Dilithium in IBM Hybrid Cloud for Logistics, offering clients early adoption options.

• AWS released expanded PQC key exchange support in its Quantum-Safe Security SDK, with logistics clients highlighted as a priority sector.

• Zebra Technologies, a supplier of handheld scanners and IoT devices for warehouses, revealed plans to pilot PQC-based key exchange in early 2023.

The speed of these responses illustrated that major industry players recognized December 2022 as the moment when PQC moved from theory into early adoption.

Integration with Supply Chain Management Systems

The logistics IT stack—spanning ERP (SAP, Oracle), WMS (Blue Yonder), and TMS platforms—is heavily reliant on secure communications. Vendors in these areas also took steps in December:

• SAP began developing PQC-ready APIs for secure partner communications.

• Oracle SCM Cloud initiated internal testing for Kyber integration.

• Blue Yonder worked on PQC modules for its last-mile delivery optimization tools.

IBM also released PQC libraries to strengthen blockchain-backed procurement auditing and asset tracking systems, ensuring encrypted proof of authenticity for cargo in transit.

Challenges Ahead for PQC Deployment

Despite growing momentum, PQC adoption in logistics faces several hurdles:

1. Hardware constraints: Many logistics IoT devices—scanners, sensors, vehicle telematics—lack the processing power to run PQC efficiently.

2. Interoperability: Customs systems across different countries must synchronize standards to avoid bottlenecks.

3. Certification cycles: Mission-critical logistics systems can take years to re-certify after cryptographic updates.

4. Performance trade-offs: PQC algorithms often require larger key sizes and more bandwidth, which could strain real-time systems.

These challenges dominated December’s discussions. Industry leaders emphasized the need for testing frameworks, consortium-based validation, and phased migration plans.

International Cooperation Expands

The December 21 ENISA update also spurred new rounds of global cooperation:

• Japan’s NICT and Singapore’s CSA initiated bilateral talks with the EU and U.S. to harmonize PQC timelines.

• Brazil’s Intelipost expressed interest in developing Latin American cryptographic standards aligned with Kyber.

• The Global Shipping Business Network (GSBN) began evaluating PQC for blockchain-based documentation exchange.

Multilateral working groups were formed to ensure cryptographic resilience by 2030, recognizing that a fragmented approach would undermine security.

Looking Ahead

ENISA projected that full PQC adoption across logistics ecosystems would take 5–7 years, given the need to replace legacy systems and retrain cybersecurity staff. However, pilot programs were already expected to begin in mid-2023, particularly in European ports and U.S. customs networks.

NIST aims to finalize PQC standards by 2024, and industry consortia are funding training materials, testing platforms, and migration playbooks to accelerate adoption.

Conclusion

December 2022 represented a key inflection point for integrating post-quantum cryptography into global supply chain operations. With logistics systems becoming increasingly digital, automated, and interdependent, protecting them against future quantum threats is now a strategic imperative—not a distant concern.

By aligning standards across the U.S., EU, and Asia, and with major logistics companies beginning pilot programs, PQC is rapidly transitioning from research to reality. The challenge ahead lies in managing interoperability, hardware upgrades, and certification delays, but the momentum is undeniable.

As PQC adoption scales through the 2020s, supply chains will be better prepared to withstand the cryptographic disruptions that quantum computing may bring. December 2022 will be remembered as the month when quantum-safe logistics security truly began.

Quantum in Defense Logistics Gets Lift-Off

On December 16, 2022, the U.S. Space Force released a new Small Business Innovation Research (SBIR) Phase I solicitation aimed at advancing the role of quantum computing in logistics. The program targeted one of the military’s most pressing needs: ensuring resilient, adaptive, and efficient supply chain operations in contested and rapidly evolving environments.

For decades, military logistics has faced the same fundamental challenge—getting the right resources to the right place at the right time under uncertainty. Whether coordinating satellite maintenance, resupplying forces in austere conditions, or managing multi-domain transport corridors, traditional algorithms often fall short when faced with dynamic disruptions. The SBIR program directly addressed this by inviting startups and researchers to demonstrate hybrid quantum-classical methods for routing, scheduling, demand forecasting, and operational planning.

The announcement represented not only a defense-sector milestone but also a clear signal that the U.S. Department of Defense (DoD) sees quantum optimization as strategically important for the future of global logistics.

Scope of the SBIR Phase I Program

The December 2022 call outlined five key focus areas:

1. Quantum-Enhanced Demand Forecasting – Exploring how quantum machine learning could improve prediction accuracy for supplies ranging from fuel to satellite parts, especially under uncertain conditions.

2. Hybrid QAOA Routing and Scheduling – Testing quantum approximate optimization algorithms (QAOA) in real-world routing contexts, such as satellite resupply missions or multi-truck military convoys.

3. Resilient Logistics Under Contested Conditions – Developing systems capable of re-routing dynamically during cyberattacks, jamming incidents, or sudden loss of communication.

4. Integration with Digital Twin Platforms – Linking quantum algorithms with logistics simulations and digital twins already in use across DoD planning environments.

5. Blockchain-Backed Verification – Using blockchain to secure and verify logistics decisions, ensuring traceability and reducing risks from data tampering.

The SBIR explicitly called for hybrid models—those combining classical optimization with quantum subroutines—since fully fault-tolerant quantum systems remain years away. This pragmatic framing opened the door for companies already experimenting with NISQ-era hardware.

Why December 2022 Matters

This SBIR solicitation was historic because it marked the first U.S. defense funding initiative specifically targeted at quantum logistics. Previous government interest in quantum computing had largely focused on cryptography, sensing, and materials science. Logistics had remained a secondary theme.

But December 2022 was a turning point. Only weeks earlier, QC Ware and Aisin had demonstrated hybrid reinforcement learning and QAOA applied to real vehicle-routing data. By aligning with such breakthroughs, the Space Force positioned itself to capture private-sector innovation and test it under defense-grade conditions.

The move also underscored a recognition that logistics is now a contested domain. In space and terrestrial theaters alike, the ability to predict, adapt, and execute supply operations can define mission success. Quantum optimization promises to provide precisely that competitive edge.

Industry and Academic Reactions

The SBIR immediately caught the attention of both startups and research institutions. Companies including Zapata, Quantum Computing Inc., QC Ware, and ColdQuanta welcomed the initiative, seeing it as validation of years of work on logistics algorithms.

On the academic front, labs at MITRE, Georgia Tech, University of Illinois, and University of Southern California’s ISI began exploratory studies with an eye toward proposal submission. Some universities even convened joint workshops with aerospace contractors to align their research agendas with SBIR requirements.

Internationally, defense observers in Europe and Asia viewed the announcement as a bellwether. Australia’s defense technology innovation office and the UK’s Ministry of Defence both initiated their own exploratory reviews of quantum logistics pathways in early 2023, citing the Space Force’s leadership as a catalyst.

Real-World Use Cases in the SBIR

The solicitation outlined several scenarios where hybrid quantum-classical methods could play a transformative role:

• Space-Based Supply Forecasting: Anticipating spare-part demand across constellations of satellites orbiting at different altitudes and subject to variable radiation damage.

• Ground-to-Orbit Launch Support: Optimizing the scheduling of rockets, payload preparation, and ground crew logistics under narrow launch windows.

• Terrestrial Resupply in Contested Environments: Coordinating convoys or drone deliveries in regions where cyber or space-based disruptions limit communication and GPS accuracy.

Each scenario tested the robustness of algorithms against uncertainty, dynamic reconfiguration, and adversarial interference—conditions where traditional optimization falters.

Integration Challenges and Opportunities

One of the largest hurdles identified was interfacing quantum algorithms with existing DoD systems, such as the Joint Logistics Enterprise (JLEnt) and TRANSCOM’s planning platforms. These legacy systems are deeply entrenched and not easily adapted to new computational paradigms.

The SBIR encouraged solutions involving middleware and APIs, enabling quantum routines to slot into broader simulation and decision-support platforms. Another opportunity lies in simulation sandboxes—secure environments where logistics planners can compare classical versus hybrid solutions before field deployment.

The solicitation also placed emphasis on cybersecurity and verification. In defense logistics, every routing decision carries risk. Hybrid workflows would therefore need hardened interfaces, auditable decision logs, and layered verification protocols.

Global Implications

The announcement reverberated worldwide. Within days of the SBIR’s release, European defense consortia began exploring how NATO might coordinate on quantum logistics. Germany’s Bundeswehr announced a review of its logistics modernization roadmap to consider quantum algorithms.

Meanwhile, Japan’s Ministry of Economy, Trade, and Industry (METI) quietly expressed interest in monitoring U.S. military quantum logistics experiments as part of a broader 2023 innovation exchange with the Pentagon. The ripple effect illustrated how closely allied nations are watching U.S. defense initiatives in quantum.

Next Steps and Funding Pathway

The Phase I program offered up to $250,000 in initial funding for feasibility studies, with Phase II awards projected at $1 million or more for prototypes. Applicants had until early 2023 to submit proposals, and awards were expected in Q1.

Likely contenders included hybrid AI/quantum companies with prior Department of Energy or DARPA contracts, as well as aerospace contractors teaming with quantum software startups. The Phase I outcomes would determine which solutions advanced toward real pilot deployments within operational logistics systems.

Conclusion

December 16, 2022, marked a critical milestone in the evolution of quantum logistics. By launching the SBIR Phase I program, the U.S. Space Force signaled its intent to bring quantum optimization out of the lab and into one of the most demanding arenas—defense logistics.

The initiative linked cutting-edge research with operational needs, spanning terrestrial resupply, space launch planning, and satellite fleet management. Its emphasis on hybrid quantum-classical systems reflected a pragmatic recognition of today’s hardware limits, while still pushing boundaries toward transformative applications.

In doing so, the Space Force positioned the United States as a global leader in quantum logistics, setting off ripples across academia, industry, and allied defense organizations. As Phase I progresses into Phase II, the program could very well establish the blueprint for integrating quantum algorithms into critical national and international supply chains.

December 2022 therefore stands as the month when quantum logistics entered the defense mainstream—ushering in a new era where quantum resilience and adaptability may define the future of global military operations.

Hybrid Quantum-Neural Systems Step into Real Logistics

QC Ware, a Palo Alto–based quantum software company, and Aisin Corporation, a Tokyo-headquartered automotive parts and logistics giant, jointly published a whitepaper on arXiv that could reshape the way global logistics networks are optimized. The paper, titled “Quantum Neural Networks for a Supply Chain Logistics Application,” introduced a hybrid quantum-classical reinforcement-learning (RL) architecture designed to tackle complex vehicle-routing problems.

At its core, the system integrated quantum circuits into neural network layers, creating what the authors termed a quantum orthogonal neural network (QONN). This model directly engaged with routing scenarios drawn from Aisin’s real logistics datasets—spanning multi-truck dispatch, delivery scheduling, and cargo balancing challenges typical in the automotive supply chain.

For decades, vehicle-routing has been one of the most computationally challenging problems in logistics, often classified as NP-hard. Traditional heuristics and metaheuristics work for small cases but struggle to scale in dynamic, high-volume environments. By embedding quantum operations within reinforcement-learning policies, QC Ware and Aisin demonstrated a novel method for tackling routing complexity, one that blended today’s classical machine-learning capabilities with tomorrow’s quantum acceleration.

Why December 2022 Was a Turning Point

This milestone marked the first time a hybrid quantum-reinforcement-learning algorithm was tested on industrial logistics data, rather than artificial or simplified models. The December 2022 publication represented a transition from theoretical simulations to applied logistics pilots.

Historically, much of quantum optimization research has centered on abstract benchmarks—graph cuts, max-cut, or random constraint satisfaction problems. While valuable for testing algorithms, these benchmarks fail to capture the uncertainties and constraints of real logistics, such as varying traffic conditions, delivery deadlines, fuel consumption, and warehouse capacity.

QC Ware and Aisin’s collaboration bridged this gap, producing results that were directly interpretable by logistics managers. The fact that hybrid RL models achieved routing performance comparable to seasoned human planners underscored quantum computing’s potential to become not just an academic curiosity, but a tool for operational decision-making.

Global Collaboration Across Continents

The project’s success rested on cross-continental collaboration between North America and Asia:

• Aisin Corporation (Japan): Provided real automotive logistics datasets, as well as decades of operational expertise in parts supply, delivery routing, and hub-and-spoke distribution. As one of Toyota’s largest affiliates, Aisin operates a vast logistics network, making it an ideal testbed for cutting-edge technologies.

• QC Ware (USA): Led algorithmic development, hybrid system design, and simulation work. With access to NISQ-era (Noisy Intermediate-Scale Quantum) hardware through partnerships with major cloud providers, QC Ware embedded quantum circuits within reinforcement-learning frameworks.

This bi-regional cooperation highlighted how quantum logistics is emerging as a global endeavor, requiring contributions from both domain experts in supply chains and specialists in quantum algorithmics.

Technical Architecture & Insights

At the heart of the collaboration was the quantum orthogonal neural network (QONN). Its design included:

• Quantum Embedding Layers: Input data representing delivery routes, vehicle capacity, and timing constraints were mapped into quantum states, enabling the system to leverage quantum parallelism.

• Attention-Based Integration: Quantum outputs were fused with classical RL layers through attention mechanisms, allowing the system to weigh routing alternatives adaptively.

• Reinforcement Learning Environment: The model received rewards for route efficiency, fuel usage reduction, and timely deliveries, while penalizing delays or excessive idle time.

• NISQ Compatibility: The system was engineered to run on noisy intermediate-scale devices, ensuring feasibility even without fault-tolerant quantum computers.

This hybrid structure allowed the RL agent to explore vast routing options more efficiently than classical methods alone, balancing exploration with exploitation in ways classical RL often struggles to achieve at scale.

Results & Operational Performance

The pilot study yielded promising results:

• Comparable Efficiency to Manual Planning: Routes generated by the QONN-RL system matched the efficiency of human planners with years of experience.

• Load Balancing Improvements: The model demonstrated stronger adaptability in balancing truck capacity and deliveries across multiple depots.

• Scalability Potential: The architecture showed linear scaling with added delivery nodes, hinting at advantages over classical heuristics as datasets expand.

Although the system did not yet outperform all classical optimization techniques, its performance under real logistics conditions was a significant validation of hybrid quantum-RL models. Researchers emphasized that as quantum hardware scales beyond 50-qubit processors, advantages will likely become more pronounced.

Tech-Logistics Ecosystem Impact

The December 2022 release immediately caught the attention of logistics and quantum stakeholders alike:

• Academia: Research groups in Europe and the U.S. cited the work as a pioneering example of reinforcement-learning and quantum hybridization applied outside theoretical contexts.

• Industry: Automotive and aerospace firms began exploring similar hybrid models for routing and scheduling.

• Government: U.S. defense logistics research teams reportedly studied the model for potential deployment in fleet coordination and resource allocation.

By demonstrating feasibility, QC Ware and Aisin positioned quantum-RL hybrids as a contender in the race to modernize logistics systems under rising global supply chain pressures.

Broader Applications in Supply Chain & Freight

The methods demonstrated in this project extend beyond automotive logistics. Potential applications include:

• Just-In-Time Manufacturing: Reducing bottlenecks by dynamically routing parts shipments.

• Port Container Logistics: Coordinating crane movements, container stacking, and vessel scheduling.

• Intermodal Freight: Optimizing handoffs between rail, road, and sea transport.

• Last-Mile Delivery: Enhancing urban logistics for e-commerce and parcel distribution.

In each case, reinforcement-learning combined with quantum embeddings could deliver adaptive, scalable optimization that responds to real-world variability.

What Comes Next?

Looking ahead, QC Ware and Aisin outlined a roadmap involving:

1. Scaling Circuit Depth and Width: Expanding quantum layers as hardware improves beyond NISQ limitations.

2. Integration with Logistics Software: Embedding quantum-RL models within existing transport management systems.

3. Pilot Deployments: Extending the model from simulated datasets into live operations at selected Aisin hubs.

4. Government Partnerships: Aisin has reportedly explored collaborations with Japanese ministries for smart-hub and automated dispatch programs.

Such steps indicate that quantum-RL systems may move from research to live operational deployment within the next three to five years.

Conclusion: From Theory to Applied Quantum Logistics

December 5, 2022, will be remembered as a watershed moment in the history of quantum logistics. For the first time, a hybrid quantum-reinforcement-learning system was applied to real-world vehicle routing, bridging the gap between abstract theory and practical logistics.

QC Ware and Aisin’s collaboration showed that even in today’s NISQ era, quantum models can complement classical methods to deliver competitive performance in industrial contexts. While the system is not yet fully superior to advanced classical algorithms, its adaptability and scalability suggest that quantum-RL hybrids will become indispensable as supply chains grow more complex.

This pilot not only advanced academic understanding but also set a foundation for commercial and governmental adoption. If progress continues along the outlined roadmap, the next decade could see quantum-enhanced logistics becoming a standard feature in global supply chains.

The logistics industry has long faced one of computer science’s hardest challenges: how to optimize vehicle routes across vast, multi-node networks in real time. These problems—classified as NP-hard—become exponentially more difficult as variables increase. Traditionally, human planners or advanced heuristics have kept freight networks running smoothly, but December 2022 introduced a new frontier. On December 5, researchers from QC Ware and Aisin Corporation released a whitepaper on arXiv, demonstrating the first real-world test of a Quantum Approximate Optimization Algorithm (QAOA) integrated within reinforcement-learning frameworks for industrial vehicle routing.

This research signaled more than a theoretical advance—it was a proof-of-concept that quantum techniques could enhance the routing engines underpinning global supply chains. By embedding quantum circuits into reinforcement-learning attention layers, the hybrid model successfully produced routing results comparable to Aisin’s expert human dispatchers.

QAOA Meets Combinatorial Logistics

QAOA, introduced by Farhi et al. in 2014, has been widely regarded as a promising approach for optimization on near-term quantum devices. Unlike fully fault-tolerant algorithms, QAOA is designed to work on noisy intermediate-scale quantum (NISQ) hardware by approximating optimal solutions to combinatorial optimization problems.

In the December 2022 study, QC Ware researchers embedded QAOA layers inside a reinforcement-learning (RL) agent trained to solve complex vehicle routing problems. This was not a toy model or academic abstraction: the dataset came directly from Aisin’s automotive logistics operations, including multi-depot delivery points, fluctuating demand patterns, and multiple truck constraints.

The combination of QAOA and RL allowed the system to escape local minima that typically trap classical heuristics, producing solutions with a robustness previously unseen in comparable AI-only models.

Why December 2022 Represents a Milestone

The release of this paper marked the first public demonstration of QAOA applied at scale to an industrial dataset in logistics. Earlier experiments had focused on small synthetic datasets, but the Aisin collaboration grounded theory in reality.

For the logistics industry, the significance was clear: this was a stepping stone toward quantum-ready routing engines capable of integrating directly with enterprise logistics platforms. Unlike speculative projections, the results showed measurable performance gains with practical impact.

Real-World Data, Real Results

Aisin Corporation, a Toyota Group company and one of Japan’s largest automotive parts suppliers, brought authenticity to the project by contributing detailed logistics datasets. These included:

• Multi-truck routing demands with varied capacities.

• Depot networks spanning multiple regions.

• Dynamic demand patterns changing across delivery windows.

Using this data, the hybrid RL+QAOA model was benchmarked against Aisin’s own dispatcher plans. Results revealed comparable efficiency in route optimization, effective load balancing, and adaptability to changing conditions. In some trials, the hybrid system outperformed classical heuristics by exploring alternative routing pathways beyond conventional assumptions.

Global Collaboration and Cross-Sector Momentum

This was not just a Japan-U.S. collaboration but part of a broader movement linking academia, industry, and government.

• North America (USA): QC Ware, headquartered in Palo Alto, provided algorithmic innovation, QASM simulation environments, and integration of QAOA into hybrid RL structures.

• Asia (Japan): Aisin provided operational data and industry context, ensuring the research remained relevant to real-world supply chain challenges.

The partnership reflects a growing recognition across continents that logistics is a prime candidate for early adoption of quantum computing.

Technical Breakdown: Hybrid QAOA Architecture

The researchers described the system as a QAOA-enhanced reinforcement-learning agent with four critical components:

1. Problem Decomposition: Large routing challenges were broken into sub-instances manageable by limited qubit counts.

2. Quantum Embedding in Attention Heads: QAOA circuits were embedded into neural attention layers, guiding the RL agent’s decision space.

3. Reinforcement Training: Reward functions were designed around minimizing travel time, balancing loads, and avoiding inefficiencies.

4. Constraint Handling: Hybrid classical-quantum feedback loops ensured adherence to hard logistics constraints such as truck capacity.

This architecture allowed the system to run effectively on current NISQ simulators while being future-proofed for larger qubit hardware.

Benchmarking and Comparative Analysis

In controlled benchmarks, the hybrid model demonstrated performance comparable to Aisin’s human dispatchers and classical heuristics. Notably, the QAOA component allowed the system to avoid common pitfalls of greedy algorithms, achieving solutions that were closer to global optima.

Compared with traditional approaches:

• Classical RL alone: Prone to local minima and lacked robustness under shifting conditions.

• QAOA alone: Limited by qubit counts and noise sensitivity.

• Hybrid model: Achieved the best balance of adaptability, efficiency, and scalability.

Ecosystem Influence and Academic Uptake

The publication reverberated far beyond Aisin’s supply chain. Researchers across Europe, North America, and Latin America cited the study in new proposals to apply hybrid quantum methods to container port scheduling and air cargo optimization.

In the U.S., the Space Force mentioned such hybrid architectures in their Small Business Innovation Research (SBIR) calls, reflecting defense interest in quantum-assisted logistics.

Broader Industry Implications

The demonstrated system has implications across multiple logistics domains:

• Port container logistics – routing and crane allocation.

• Intermodal freight planning – optimizing truck-rail-ship interfaces.

• Just-in-time manufacturing – synchronizing deliveries to assembly lines.

• Last-mile delivery – dynamically adapting routes to urban traffic.

Each of these areas shares the same combinatorial complexity that QAOA-based hybrids are well-suited to tackle.

Challenges and Quantum-Ready Pathways

Despite the progress, limitations remain:

• Hardware constraints: Current NISQ devices are capped by noise and low qubit counts.

• Integration challenges: Embedding quantum layers into enterprise systems requires custom APIs and careful validation.

• Benchmarking standards: The lack of industry-wide metrics makes it difficult to consistently measure quantum advantage.

Addressing these gaps will determine how quickly such systems transition from pilots to production.

What’s on the Horizon

Post-publication, QC Ware and Aisin outlined several next steps:

• Pilot programs: Expanding trials within Aisin’s supply chain.

• Scaling circuits: Leveraging upcoming 50-100 qubit devices.

• Toolkits: Extending QC Ware’s Forge platform to support QAOA-RL integration.

• Global collaboration: Extending to European and North American logistics hubs.

Conclusion

December 5, 2022, marked a watershed in the history of logistics optimization. For the first time, QAOA was applied to a real-world industrial dataset through a hybrid reinforcement-learning framework, bridging the gap between theoretical quantum algorithms and operational logistics.

The demonstration by QC Ware and Aisin showed that hybrid quantum-classical models are not just experimental curiosities—they are practical tools with the potential to revolutionize routing, freight planning, and global supply chain management. As hardware scales and standards mature, the logistics industry may soon find itself navigating with quantum-enhanced maps—more adaptive, efficient, and future-ready than ever before.

On November 28, 2022, Europe’s maritime logistics sector reached a new milestone in cybersecurity. At the Port of Rotterdam, a consortium of port authorities, logistics firms, and quantum security companies officially launched the first live trial of a quantum-safe blockchain designed for critical port operations. The pilot integrated XMage’s hybrid cryptographic modules with blockchain infrastructure to secure customs declarations, bills of lading, and container tracking against the looming threat of quantum-enabled cyberattacks.

The trial marks the first time that NIST-aligned post-quantum cryptography (PQC) has been implemented in live maritime logistics workflows, setting a precedent for how the shipping and supply chain industry may safeguard itself in the quantum era.

Post-Quantum Blockchain at the Port of Rotterdam

The pilot was coordinated by Portbase, the digital backbone of Dutch ports, in partnership with EuropeChain, a blockchain infrastructure provider specializing in federated ledger deployments. Together, they applied XMage’s hybrid cryptographic module to secure port communications in real-time.

The system was deployed to safeguard a range of essential transactions, including:

• Customs Declarations – sensitive import/export paperwork that is often the target of fraud.

• Bills of Lading – legal transport documents critical for international shipping.

• Container Tracking Data – real-time shipment updates vulnerable to interception or tampering.

All data exchanges were stored in a federated blockchain ledger hosted across multiple European data centers, ensuring redundancy and resilience. What made this deployment groundbreaking was the integration of post-quantum cryptography algorithms directly into live blockchain operations.

Why Quantum-Safe Logistics Is Needed Now

Maritime logistics underpins more than 90% of global trade volume, making it a prime target for cyberattacks. In recent years, ransomware attacks on shipping giants like Maersk and supply chain disruptions from cyber incidents have exposed the vulnerability of the industry.

The rise of quantum computing research by nation-states adds a new layer of urgency. A sufficiently powerful quantum computer could break classical encryption protocols such as RSA or ECC, rendering current blockchain-based clearance and documentation systems insecure.

With critical logistics data at stake, European regulators and industry leaders are accelerating efforts to adopt quantum-safe protocols before quantum capabilities mature. The Rotterdam pilot represents one of the first applied steps in this transition.

XMage and EuropeChain Collaboration

XMage, a Dutch cybersecurity company spun out of TU Delft’s quantum research lab, developed the cryptographic module that made this pilot possible. The company specializes in post-quantum hybrid cryptography, which allows for a gradual migration from classical to quantum-resistant systems.

Their partner, EuropeChain, provided the blockchain infrastructure that supports container logistics workflows across Rotterdam and other European ports. Together, they designed a quantum-resistant blockchain stack capable of handling thousands of transactions per second without compromising efficiency.

November’s launch marked the first live demonstration of a quantum-safe port ledger in Europe, positioning XMage and EuropeChain as leaders in the race toward secure global logistics.

Technical Architecture and NIST Alignment

The technical foundation of the trial was a hybrid encryption model. Messages and transactions were simultaneously protected by classical algorithms (like ECC) and post-quantum cryptography schemes, ensuring backward compatibility with existing systems while adding future-proof security.

Specifically, XMage deployed:

• CRYSTALS-Dilithium – used for digital transaction signatures.

• Kyber – applied for encryption and symmetric key exchanges.

Both algorithms were selected by the U.S. National Institute of Standards and Technology (NIST) in July 2022 as finalists for standardization under its global post-quantum cryptography program. Aligning with these standards ensured that the trial adhered to the most widely recognized PQC benchmarks.

Pilot Results and Industry Feedback

The trial ran for 30 days and focused on securing digital freight contracts and port scheduling documentation. Industry leaders including Maersk, MSC, and CMA CGM observed and monitored performance metrics during the trial period.

Early reports showed:

• 10% increase in document validation efficiency due to faster verification of digital signatures.

• No measurable latency increase, despite the added complexity of hybrid encryption.

• Seamless interoperability with existing customs clearance systems, which was critical for real-world feasibility.

These results indicate that quantum-safe blockchain systems can be adopted without slowing down logistics operations—an essential requirement for ports handling millions of containers per year.

Geopolitical and Regulatory Implications

The European Union has been proactive in preparing for quantum-era cybersecurity. Under the Digital Operational Resilience Act (DORA)—set to take effect in 2025—critical infrastructure operators will be required to adopt robust cryptographic standards. Quantum-safe systems are widely expected to become a compliance requirement under DORA and related EU cybersecurity directives.

By launching this trial, Rotterdam positions itself ahead of regulatory timelines while setting an example for other major European ports. Already, Hamburg, Antwerp, and Valencia have announced plans to conduct similar pilots in 2023.

This move also strengthens Europe’s technological sovereignty, reducing reliance on U.S. and Asian tech providers by fostering homegrown solutions like XMage.

Toward a Quantum-Ready Maritime Industry

The trial also demonstrates how the maritime sector is preparing for broader quantum readiness. Beyond blockchain security, researchers are exploring:

• Quantum optimization algorithms for container allocation and vessel routing.

• Quantum sensing applications for port surveillance and cargo integrity checks.

• Quantum communication protocols for secure ship-to-port data links.

Together, these innovations represent a multi-pronged strategy for ensuring Europe’s shipping hubs remain both secure and competitive in the coming decades.

Challenges and Next Steps

While the pilot delivered promising results, several challenges remain before full deployment:

• Integration Complexity – Large ports operate with dozens of legacy IT systems, making end-to-end PQC adoption gradual.

• Hardware Requirements – PQC schemes like Dilithium and Kyber can require more processing power than classical cryptography.

• Global Coordination – To achieve full security, international ports and customs agencies must adopt similar protocols to prevent weakest-link vulnerabilities.

The Rotterdam consortium has already announced plans to expand the trial to cover 20% of container flows by mid-2023, with the goal of full adoption by 2025 in line with DORA regulations.

Conclusion

The November 28, 2022 quantum-safe blockchain trial at the Port of Rotterdam represents a historic step in securing global maritime logistics against the quantum threat. By integrating XMage’s hybrid cryptography with EuropeChain’s blockchain platform and aligning with NIST’s PQC standards, the consortium demonstrated that quantum-safe logistics systems are both feasible and efficient.

As Europe prepares for stricter cybersecurity mandates and quantum technologies inch closer to practical reality, Rotterdam’s pilot provides a blueprint for other global ports. It highlights how industry innovation, regulatory foresight, and academic research can converge to safeguard supply chains in an era of technological disruption.

In the coming years, similar trials across Hamburg, Antwerp, and Valencia will expand the quantum-secure logistics network, ensuring that Europe’s ports remain not only trade gateways but also fortresses of digital resilience.

Introduction

Quantum computing has long been hailed as a technology that could transform industries requiring massive computational power. Among the most promising sectors is logistics, where optimization problems are notoriously difficult to solve using classical systems. On November 21, 2022, Pasqal, a French startup specializing in neutral atom-based quantum processors, announced a groundbreaking partnership with Saudi Arabia’s King Abdullah University of Science and Technology (KAUST).

This collaboration is aimed at exploring the role of quantum computing in advancing supply chain optimization across Saudi Arabia’s rapidly expanding logistics, industrial, and manufacturing hubs. Beyond technical goals, the agreement is significant in a geopolitical sense: it reflects the growing alignment between European deep-tech startups and Middle Eastern research and development institutions seeking to build future-ready economies.

Pasqal’s Technology: Neutral Atoms for Near-Term Quantum Value

Pasqal, founded in 2019 by French physicist Alain Aspect (Nobel Prize in Physics, 2022) and colleagues, has quickly become one of the most visible quantum startups in Europe. Unlike companies focused on superconducting qubits or trapped ions, Pasqal uses neutral atom arrays—ultracold atoms trapped and manipulated by lasers—to build its processors.

This approach has several advantages. Neutral atom processors offer high connectivity between qubits, low crosstalk, and potential scalability into the hundreds or thousands of qubits. These features are critical for solving combinatorial optimization problems, which are central to logistics applications. Supply chain challenges such as vehicle routing, dynamic scheduling, and real-time resource allocation fall into NP-hard problem classes. Pasqal’s platform is well-suited to explore these scenarios, especially in hybrid quantum-classical configurations.

By collaborating with KAUST, Pasqal gains access to regional industrial case studies that can serve as testing grounds for demonstrating practical quantum value in the logistics sector.

KAUST’s Role in Saudi Arabia’s Vision 2030

KAUST, located on the Red Sea coast, was established in 2009 as part of Saudi Arabia’s push to create a global center for scientific excellence. Under the Kingdom’s Vision 2030 plan, KAUST has become a cornerstone institution for developing cutting-edge research across AI, advanced materials, and logistics systems.

One of KAUST’s leading initiatives is its Smart-Logistics Innovation Lab, which integrates digital twins, AI-driven forecasting, and sensor-enabled supply chain monitoring. These systems have already been deployed in economic megaprojects such as NEOM and King Abdullah Economic City (KAEC). By introducing quantum computing into this framework, KAUST aims to position Saudi Arabia as a global testbed for future logistics infrastructure.

For the Kingdom, this partnership supports a broader diversification strategy. Logistics is identified as a pillar of the National Industrial Development and Logistics Program (NIDLP), which is tasked with turning Saudi Arabia into a regional trade and transport hub.

Quantum Use Cases in Supply Chain Optimization

The Pasqal-KAUST collaboration focuses on concrete applications of quantum computing in logistics. Among the priority use cases are:

1. Demand Forecasting and Inventory Optimization – Using quantum algorithms to model uncertain demand scenarios for spare parts and goods distribution.

2. Vehicle Routing Problem (VRP) – Quantum solvers could evaluate thousands of possible delivery routes in near real-time, significantly reducing costs and emissions.

3. Port Scheduling – Managing container berthing and yard logistics at Jeddah Islamic Port and Red Sea Gateway Terminal.

4. Multi-Objective Planning – Balancing efficiency, cost, and sustainability goals simultaneously, a task where classical methods struggle.

Early-stage work will use quantum-inspired heuristics that run on classical hardware while preparing to migrate to Pasqal’s neutral atom processors as they scale in performance.

Joint Research Goals and Milestones

To structure the collaboration, Pasqal and KAUST have set out a phased roadmap.

• Phase 1 (Late 2022–2023): Establish joint teams, benchmark classical vs. quantum-inspired solutions, and define logistics scenarios of national importance.

• Phase 2 (2023–2024): Deploy hybrid solvers via Pasqal’s Quantum Computing-as-a-Service (QCaaS) platform and publish comparative studies.

• Phase 3 (2024 onward): Integrate quantum modules into existing logistics platforms such as ERP (SAP, Oracle) and digital twin systems at KAUST’s Smart-Logistics Lab.

The first benchmark results are expected to be released publicly in mid-2023, providing early insights into whether quantum logistics solutions can outperform classical methods in real-world conditions.

European-Middle Eastern Quantum Ecosystem Emerges

This partnership highlights a growing trend: European quantum startups are forming alliances with Middle Eastern institutions eager to leapfrog into the future of computing. For Pasqal, the collaboration represents both a market expansion and a validation of its technology’s industrial relevance.

France has positioned itself as one of Europe’s quantum leaders, with a €1.8 billion national strategy launched in 2021. Meanwhile, Saudi Arabia is investing heavily in frontier technologies to reduce its dependence on oil revenues. Quantum logistics, with its dual potential for efficiency gains and sustainability benefits, is emerging as a natural convergence point for both parties.

Funding and Strategic Context

The project is supported by multiple funding streams. KAUST is contributing through its Industry Collaboration Program, which aligns academic research with commercial priorities. On the European side, Pasqal is leveraging export grants designed to accelerate high-impact international deployments of French quantum technologies.

At the national level, the initiative dovetails with Saudi Arabia’s NIDLP, which has earmarked over $30 billion for logistics and industrial infrastructure by 2030. Quantum-enhanced optimization tools could provide a competitive edge for Saudi ports, air freight hubs, and integrated land transport corridors envisioned under Vision 2030.

Challenges and Considerations

Despite optimism, the deployment of quantum logistics solutions remains in its early stages. Pasqal’s processors, like all current platforms, are in the NISQ (Noisy Intermediate-Scale Quantum) era. This means algorithms must be carefully adapted to deal with noise and limited qubit counts.

Integration with existing enterprise software poses another challenge. Large-scale supply chains run on ERP platforms that are deeply entrenched in workflows. For quantum tools to gain adoption, middleware and hybrid solutions must ensure compatibility with existing systems.

KAUST and Pasqal are addressing these issues by adopting a staged approach—starting with quantum-inspired solutions, moving toward hybrid quantum-classical systems, and only later deploying full-scale quantum algorithms as hardware matures.

A Model for International Quantum Collaboration

The Pasqal-KAUST deal is significant not only for logistics but also as a demonstration of international quantum diplomacy. France, already a European leader in quantum science, is exporting expertise and fostering partnerships abroad. Saudi Arabia, meanwhile, is signaling that it is prepared to adopt and co-develop frontier technologies at scale.

If successful, the collaboration could serve as a model for other cross-border initiatives in quantum supply chain optimization. It may also inspire similar partnerships between other Middle Eastern innovation hubs and European or North American quantum startups.

Conclusion: A Quantum Leap for the Middle East

The November 21, 2022 agreement between Pasqal and KAUST represents a turning point in the application of quantum computing to real-world logistics. While challenges remain, the collaboration demonstrates how quantum technologies are beginning to escape the lab and address pressing industrial problems.

For Saudi Arabia, the deal fits squarely into its Vision 2030 blueprint, supporting the Kingdom’s ambition to become a global logistics hub. For Pasqal, it is a chance to validate its neutral atom technology on the international stage and demonstrate near-term value in supply chain optimization.

As the partnership progresses, the Middle East may emerge as one of the first regions to deploy quantum logistics at scale. If successful, this collaboration could serve as a template for future quantum-powered logistics ecosystems worldwide.

In a significant move for the convergence of quantum computing and industrial supply chains, Airbus, Capgemini, and BMW Group came together on November 14, 2022 to announce the formation of the Quantum Logistics Consortium (QuLog). The consortium, headquartered in Toulouse, France, is supported by the Horizon Europe program and the EU Quantum Flagship initiative, placing it at the heart of Europe’s strategy to integrate cutting-edge digital technologies into critical industries.

The QuLog project is not merely exploratory research—it represents an applied, multi-year roadmap to implement quantum optimization within aerospace and automotive logistics systems. By doing so, it seeks to address some of the most pressing computational bottlenecks in the coordination of global supply chains, including real-time routing, scheduling, and inventory distribution.

The Founding Members: Airbus, Capgemini, BMW Group

The QuLog Consortium unites three influential European players.

• Airbus contributes its expertise in aerospace logistics, including aircraft manufacturing, maintenance, repair, and overhaul (MRO). Airbus operates complex supply networks across continents, where even minor inefficiencies can translate into millions of euros in costs and significant operational delays.

• Capgemini, one of Europe’s largest IT and consulting firms, brings deep capabilities in algorithm development, cloud integration, and digital transformation consulting. Capgemini is also home to several quantum labs in Paris and Munich, which are directly contributing to the project’s algorithmic and software layers.

• BMW Group, a leader in automotive manufacturing, offers use cases from its intricate global production systems. Its supply chains rely on multi-tiered suppliers, just-in-time deliveries, and increasingly electrified production lines, making logistics optimization both highly challenging and vital to competitive success.

Why Quantum for Logistics?

Classical computing has advanced logistics dramatically, but as problems scale—whether in multi-modal routing, time-constrained scheduling, or inventory optimization—the computational requirements often become intractable. Many of these problems fall into NP-hard categories, meaning classical systems cannot solve them efficiently at industrial scale.

Quantum computing, particularly through algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolvers (VQE), promises to unlock efficiencies by exploring vast solution spaces in ways impossible for classical systems.

By adopting hybrid approaches—where quantum processors augment classical optimization solvers—QuLog aims to deliver tangible improvements in areas like:

• Spare parts routing for aircraft maintenance.

• Fleet scheduling across multi-hub networks.

• Just-in-time inventory placement for automotive production.

• Supply chain resilience against disruptions.

Algorithmic Development Under Horizon Europe

The Horizon Europe framework funds the QuLog project not for hardware research, but for algorithmic and applied breakthroughs. This distinction underscores the EU’s belief that logistics is a near-term domain where quantum computing can create measurable impact.

The consortium will work with Fraunhofer Institute and the Leibniz Supercomputing Centre to benchmark performance, develop hybrid simulation environments, and ensure that results are scalable across industries. Early development efforts are focused on:

• Hybrid quantum-classical solvers for large routing problems.

• Quantum-enhanced scheduling algorithms for production systems.

• Cloud-based emulation environments to validate quantum logistics solutions before full deployment.

Airbus and BMW Use Cases

For Airbus, the primary target lies in optimizing maintenance logistics. Aircraft fleets require constant part replacements, which must be timed and positioned with precision. Mismanagement here can ground flights and incur steep financial penalties. By deploying probabilistic demand models with quantum-enhanced optimization, Airbus hopes to minimize downtime and improve turnaround efficiency at its hubs in Hamburg, Toulouse, and Tianjin.

BMW, meanwhile, is interested in applying quantum models to its global production logistics. As automotive production decentralizes and integrates new supply requirements for electric vehicle components, predicting inventory needs across hundreds of suppliers becomes increasingly complex. QuLog’s algorithms could help anticipate demand fluctuations more accurately, reducing bottlenecks and optimizing warehouse utilization.

Strategic Importance for Europe

The launch of QuLog is not just about technological progress; it reflects the European Commission’s strategic priorities. Logistics modernization is central to Europe’s twin transitions of digitalization and sustainability.

• Digitalization: QuLog demonstrates Europe’s intent to lead in applying quantum computing beyond academia, directly into real-world industrial ecosystems.

• Sustainability: By improving efficiency in logistics, quantum optimization reduces wasted fuel, lowers emissions, and supports EU climate commitments under the Fit for 55 legislative framework.

Moreover, the EU Quantum Flagship, a €1 billion, decade-long initiative, explicitly cites logistics optimization as a domain where early quantum applications will emerge. QuLog thus operates at the core of European industrial and policy alignment.

Timeline and Milestones

The QuLog project is structured in phases from late 2022 through 2025.

• Phase 1 (2022–2023): Define aerospace and automotive use cases, benchmark with quantum emulators, and develop prototype algorithms.

• Phase 2 (2023–2024): Begin pilot deployments in Airbus and BMW facilities, supported by Capgemini’s quantum labs.

• Phase 3 (2025): Scale results, release a hybrid routing optimizer, develop a cloud-native inventory planner, and publish joint policy white papers.

This roadmap reflects a pragmatic, incremental approach that balances experimentation with measurable milestones.

Industry-Wide Implications

While aerospace and automotive logistics are the immediate beneficiaries, the implications extend far beyond these sectors. Capgemini has already signaled interest from maritime logistics firms and freight operators who see opportunities in adapting QuLog modules for:

• Container routing at ports.

• Rail throughput scheduling.

• Maritime fuel optimization.

Furthermore, the consortium has committed to contributing select software modules to open-source ecosystems like Qiskit and PennyLane, ensuring that innovations can be leveraged across industries.

Challenges and Competitive Landscape

Despite its promise, QuLog faces substantial challenges.

• Quantum hardware limitations: Current systems are still in the NISQ (Noisy Intermediate-Scale Quantum) era, meaning error correction and scalability remain hurdles.

• Cross-industry alignment: Aerospace and automotive industries operate under different regulatory regimes and logistical priorities, requiring coordination to align methodologies.

• Competitive environment: Volkswagen has partnered with Xanadu, while BMW itself has experimented with QC Ware. Logistics startups like Zapata Computing and QCI are also advancing quantum routing solutions.

These dynamics make QuLog’s ability to integrate industrial, research, and governmental stakeholders particularly critical.

Conclusion: Europe’s Quantum Logistics Vanguard

The launch of the QuLog Consortium on November 14, 2022, marks a bold new chapter in Europe’s pursuit of quantum-enabled logistics. By combining the logistical sophistication of Airbus, the consulting power of Capgemini, and the supply chain complexity of BMW, the initiative positions Europe as a global leader in applied quantum logistics.

If successful, QuLog will not only reduce inefficiencies in aerospace and automotive supply chains but also serve as a template for cross-sector collaboration, accelerating adoption in maritime, rail, and freight industries.

In a world where supply chain resilience, cost efficiency, and sustainability have become mission-critical, the QuLog Consortium demonstrates how quantum computing is moving from theoretical promise to practical implementation. Europe’s leadership in this space signals not just a technological achievement, but a commitment to building smarter, more sustainable, and more resilient global logistics systems for the future.

Volkswagen has consistently positioned itself as one of the most forward-thinking automakers when it comes to adopting emerging technologies, particularly in the area of quantum computing. On November 10, 2022, the company announced a major step forward: a collaboration with Canadian quantum computing company Xanadu, a Toronto-based leader in photonic quantum processors. The initiative focuses on applying quantum algorithms to optimize urban fleet routing—a notoriously difficult logistical challenge that directly impacts both operating costs and environmental sustainability.

Volkswagen’s Quantum Journey Accelerates

Volkswagen’s interest in quantum computing dates back to 2016, when it began working with D-Wave Systems on early experiments in traffic flow optimization. Those projects showed the potential of quantum methods in solving combinatorial problems but also highlighted the need for scalable and practical hardware solutions. Six years later, Volkswagen has doubled down by collaborating with Xanadu, whose photonic quantum computing platform promises accessibility, scalability, and applicability to real-world logistics.

For Volkswagen, the partnership signals an evolution from theoretical pilot projects to practical, industrial-grade solutions. By targeting commercial vehicle routing—the lifeblood of modern logistics—the automaker is bringing quantum computing into the heart of its operations.

Focus on Urban Fleet Optimization

Urban logistics is one of the most complex and computationally demanding problems in supply chain management. Delivery vehicles, ride-sharing fleets, and public transport must operate in congested city environments where traffic, construction, and unpredictable demand create constant disruptions. Traditional algorithms often rely on heuristics or approximations, which, while efficient, cannot always account for large-scale, real-time complexity.

Volkswagen and Xanadu’s project seeks to tackle this challenge head-on using the Quantum Approximate Optimization Algorithm (QAOA) and related techniques. QAOA is designed to address combinatorial optimization problems by finding near-optimal solutions faster and more efficiently than classical methods. In this case, the algorithm will evaluate thousands of potential routes across fleets of delivery vans, buses, or shared vehicles, dynamically adjusting to real-world conditions like accidents or sudden surges in demand.

Volkswagen anticipates that this approach will not only improve efficiency but also enhance service reliability for businesses and customers who depend on timely deliveries and consistent transport schedules.

Why Partner with Xanadu?

Xanadu’s value proposition lies in its photonic quantum computing technology. Unlike many quantum processors that require extreme cryogenic cooling and costly infrastructure, Xanadu’s Borealis processor operates at room temperature using photons as carriers of quantum information. In 2022, Borealis achieved a milestone in quantum computational advantage, demonstrating the ability to perform tasks beyond the reach of classical supercomputers.

This hardware innovation makes Xanadu’s platform attractive for industrial applications like logistics, where integration into cloud environments and existing IT infrastructure is critical. Volkswagen’s strategy involves accessing Xanadu’s processors through cloud APIs, embedding them directly into its fleet management software. By doing so, the automaker avoids hardware deployment challenges while still benefiting from quantum performance gains.

Sustainability and Emissions Reduction

The logistics sector faces growing pressure to meet sustainability targets, particularly under regulatory frameworks such as the European Union’s Fit for 55 legislation. For Volkswagen, quantum routing is more than just a technological experiment—it’s a tool for achieving tangible environmental outcomes.

Internal simulations suggest that quantum-optimized routing could reduce fuel consumption by 5 to 10% per fleet, depending on the complexity of the deployment. For a fleet of thousands of vehicles, such reductions translate into significant cuts in carbon dioxide emissions. Moreover, the approach aligns with Volkswagen’s corporate sustainability goals, which include achieving net carbon neutrality by 2050 and meeting intermediate targets on emissions reductions across its global supply chain.

By linking quantum computing directly to sustainability outcomes, Volkswagen is reframing the technology not as a futuristic novelty but as a practical enabler of environmental responsibility.

Cross-Continental Collaboration

One of the notable aspects of the Volkswagen–Xanadu partnership is its cross-continental scope. The initiative is co-funded by Germany’s Federal Ministry of Education and Research (BMBF) and Canada’s National Research Council Industrial Research Assistance Program (NRC IRAP). This joint funding underscores the importance of international collaboration in scaling quantum innovation.

Initial proof-of-concept trials began in late November 2022, involving a fleet of 50 vehicles in Wolfsburg and Toronto. By testing in two very different urban environments—one a structured European city and the other a sprawling North American metropolis—Volkswagen and Xanadu aim to validate the generalizability of quantum-enhanced routing models.

Competitive Context

Volkswagen’s move must also be seen against the backdrop of intensifying competition in the automotive and logistics industries. BMW has been working with Quantum Computing Inc. (QCI) on optimization problems, while Ford has partnered with NASA on similar routing applications. However, Volkswagen’s focus on photonic quantum computing sets it apart.

Photonic systems are widely considered more scalable and practical than some other quantum architectures because they avoid the need for cryogenics and can be distributed through cloud environments more readily. By aligning with Xanadu, Volkswagen is betting on a platform that may deliver commercial viability faster than competing technologies.

Road Ahead

The Volkswagen–Xanadu project is structured in stages. The immediate goal is to scale from 50 test vehicles to 1,000 vehicles in 2023, expanding trials across additional European and North American cities. Integration with Volkswagen’s MOIA ride-sharing platform is also on the roadmap, signaling an intent to extend quantum routing benefits beyond cargo logistics to include urban passenger mobility.

In the longer term, Volkswagen envisions embedding quantum computing into its autonomous fleet coordination systems. As autonomous vehicles scale up, routing complexity will only increase, and classical algorithms may fall short in managing real-time, large-scale fleet decisions. Quantum methods could provide the backbone for fully automated, sustainable, and resilient urban mobility ecosystems.

Conclusion

Volkswagen’s November 10, 2022 partnership with Xanadu is a landmark development in the convergence of quantum computing and logistics. By targeting fleet routing—a core function of both commercial logistics and passenger transport—the initiative moves quantum computing from laboratory experiments into applied industrial practice.

The collaboration highlights several broader trends: the push for quantum practicality, the drive toward sustainable logistics, and the importance of global collaboration in scaling new technologies. While challenges remain, particularly in scaling algorithms and integrating quantum outputs with existing digital twins, the partnership demonstrates that quantum-enhanced logistics is no longer a distant prospect but a rapidly unfolding reality.

As Volkswagen scales trials from dozens to thousands of vehicles, the impact of quantum routing could reshape not only its own operations but also industry-wide approaches to mobility and sustainability. In an era where efficiency and environmental responsibility are inseparable, Volkswagen’s collaboration with Xanadu positions it as a pioneer in the next generation of quantum-powered logistics.

The logistics industry has been quick to embrace the digital revolution. Autonomous guided vehicles (AGVs), RFID-powered tracking systems, mobile robots, and vast networks of IoT-enabled sensors now form the backbone of global warehouse and distribution centers. Yet, with every technological leap comes new vulnerabilities. On October 28, 2022, Zebra Technologies announced it had begun field trials of quantum-safe cryptographic protocols within warehouse IoT systems—making it one of the first major logistics tech firms to preemptively prepare for the post-quantum era.

Partnering with the National Institute of Standards and Technology (NIST) and MIT Lincoln Laboratory, Zebra is testing lattice-based encryption and digital signatures designed to withstand attacks from future quantum computers. This project represents a crucial intersection between logistics efficiency and cybersecurity resilience—two areas increasingly seen as inseparable in modern supply chains.

Protecting IoT at the Edge in Warehousing

Smart warehouses depend heavily on IoT devices: RFID readers track pallets and inventory movement, mobile robots shuttle goods across aisles, and automated conveyor systems coordinate with edge servers. However, these devices, often lightweight in computing power and distributed across large facilities, present prime targets for cyberattacks.

Traditional cryptographic protocols—RSA and ECC—have held strong for decades. But quantum computers, once they reach sufficient scale, could theoretically crack these algorithms in hours or minutes using Shor’s algorithm. Zebra’s October 2022 trials focus on post-quantum cryptography (PQC) to ensure that logistics IoT ecosystems will remain secure long after classical protections become obsolete.

The trial integrates CRYSTALS-Kyber (lattice-based key exchange) and CRYSTALS-Dilithium (digital signatures) into Zebra’s EdgeConnect RFID readers and robotics systems. This approach ensures secure communication between devices, controllers, and warehouse management platforms.

Trial Design and Deployment

The PQC trials were rolled out across multiple U.S. Midwest retail distribution hubs, where Zebra already manages extensive IoT networks. The design included three main layers:

1. Hybrid Key Exchange
   Each communication between devices was secured using both classical RSA/ECC encryption and lattice-based Kyber. This dual protection ensured backward compatibility while testing the quantum-resistant layer.

2. Signature Verification
   Device authentication was powered by Dilithium-based signatures, which verify that IoT devices within the warehouse network were legitimate and not malicious clones.

3. Performance Evaluation
   Engineers monitored latency during device-to-device handshakes, benchmarking how quickly keys could be generated and exchanged. Early findings revealed a 20–30% increase in handshake time, but this performance impact remained well within operational thresholds for logistics systems.

Zebra’s engineering team noted that although PQC introduces extra computational weight, the balance between security and usability favored continued adoption.

Why This Matters for Supply Chain Security

The logistics sector is uniquely vulnerable to cyber disruptions. A compromised warehouse IoT system could:

• Halt operations by disabling robots or misreporting inventory.

• Corrupt tracking data, leading to cascading supply chain errors.

• Expose sensitive business information, including supplier and customer records.

By trialing quantum-safe IoT now, Zebra is ensuring that future-proof protections are baked into its systems before threats materialize.

From a strategic standpoint, this move positions Zebra as a first mover in quantum-secure logistics infrastructure. With NIST’s PQC standardization process well underway, Zebra’s live trials provide valuable feedback to the broader industry on how post-quantum algorithms perform under real operational stress.

Industry Implications

The significance of this October 28, 2022 announcement extends beyond Zebra itself:

• IoT Security Frontier: This marks one of the first real-world applications of PQC in industrial logistics settings, rather than academic or defense experiments.

• Vendor Roadmaps: Robotics integrators and warehouse automation companies are now evaluating firmware updates to embed similar protections.

• Ecosystem Push: Large retailers partnering with Zebra gain early exposure to PQC-secured supply chains, likely influencing adoption across logistics hubs in North America and beyond.

Challenges Ahead

Despite the promise, the trials also highlight challenges that must be overcome:

1. Resource Constraints
   Many IoT devices have limited compute and memory. Optimizing PQC implementations for lightweight hardware remains a significant engineering hurdle.

2. Lifecycle Management
   PQC requires secure firmware update channels, meaning logistics companies must redesign update cycles to handle new certificate systems.

3. Standardization
   The industry must coordinate protocols across vendors to prevent compatibility issues in interconnected warehouses and global supply chain platforms.

4. Long-Term Integration
   Quantum-resistant systems will need to scale across not only warehouses but also transport fleets, port terminals, and last-mile delivery networks.

Roadmap and Next Steps

Zebra has outlined a phased roadmap for its PQC strategy:

• 2023–2024: Broader firmware rollouts across IoT device families, starting with enterprise RFID readers and mobile robotics fleets.

• 2025: Expansion into edge servers and warehouse management middleware, ensuring end-to-end security across the logistics tech stack.

• Beyond 2025: Potential integration with quantum key distribution (QKD) pilots, should optical communication channels in logistics infrastructure mature.

This roadmap indicates that Zebra is not treating PQC as an isolated trial but as a core architectural shift in warehouse cybersecurity.

Strategic Value for Global Logistics

Cyber resilience is no longer an optional feature in logistics—it is an operational necessity. As geopolitical tensions and cyberattacks against supply chains increase, future-proofing logistics systems is essential to ensuring continuity of operations.

For Zebra, embedding PQC into warehouse IoT offers three major advantages:

• Customer Trust: Retailers, manufacturers, and logistics providers can rely on Zebra systems to secure operations against emerging threats.

• Market Differentiation: Being among the first to integrate PQC gives Zebra a competitive edge in the automation technology sector.

• Global Influence: Zebra’s trials feed back into NIST’s evaluation, helping shape the global standardization of PQC protocols.

Conclusion

The October 28, 2022 Zebra Technologies pilot marks a pioneering moment for cyber-resilient logistics. By trialing post-quantum cryptographic schemes within RFID readers and robotics fleets, Zebra is actively securing the backbone of smart warehouses against future quantum threats.

While performance trade-offs and standardization challenges remain, the initiative demonstrates that quantum-safe IoT is not a distant aspiration but a deployable reality. In doing so, Zebra not only protects logistics networks but also sets a benchmark for industry-wide adoption.

As supply chains become more digital, interconnected, and globalized, quantum-ready security will be a foundational requirement. Zebra’s early leadership ensures that when quantum computing matures, logistics infrastructure will not be caught unprepared—but instead fortified for a secure, efficient future.

The desert is one of the harshest logistical environments in the world. Long, unbroken routes, unpredictable weather, scarce refueling points, and dynamic resource constraints make traditional supply chain optimization a formidable challenge. For years, classical models—whether deterministic scheduling systems or modern AI-driven routing platforms—have struggled to account for the high variability inherent in such environments. On October 20, 2022, a landmark initiative was announced: the Abu Dhabi Investment Office (ADIO) revealed a strategic partnership with UK-based quantum startup Qubetron to deploy quantum-inspired logistics optimization in desert supply chains.

This collaboration marks a turning point for the Middle East, positioning Abu Dhabi as one of the first regions globally to test how quantum-inspired and hybrid quantum-classical systems can provide resilience in remote logistics operations.

Logistics Challenges in Harsh Environments

Desert operations in the UAE involve a spectrum of challenges. For example, transporting solar panels and turbines from coastal ports to inland solar farms requires careful handling across hundreds of kilometers of terrain with few established logistics nodes. Remote airfield supply chains also face unpredictability—sandstorms can delay shipments, extreme heat can degrade materials in transit, and limited infrastructure means rerouting options are minimal.

Traditional logistics optimization engines often collapse under such variables. Vehicle Routing Problem (VRP) variants that incorporate desert convection effects, mobile refueling needs, and time-sensitive delivery windows grow exponentially complex. This is precisely where Qubetron’s hybrid approach steps in, leveraging quantum-inspired heuristics to approximate solutions that classical engines fail to find efficiently.

On October 20, 2022, ADIO and Qubetron launched a 12-month pilot to model desert supply chains using digital twins enhanced by quantum-inspired optimization layers.

Building the UAE’s Quantum Logistics Capability

The initiative sits squarely within ADIO’s mission to fund transformative technologies that diversify and strengthen Abu Dhabi’s economy.

The partnership with Qubetron focused on three strategic pillars:

• Use cases identified: Applications ranged from solar farm component transport to mobile medical logistics in remote desert communities.

• Platform deployment: Qubetron’s algorithms were embedded into ADIO’s digital twin testbeds, simulating thousands of logistical scenarios under extreme constraints.

• Data integration: Datasets from Etihad Rail and Abu Dhabi Ports provided a backbone of real transport flows, enriched by real-time weather and terrain variables.

This systemic approach reflects Abu Dhabi’s strategy of building digital-first infrastructure capable of supporting new paradigms like quantum optimization without waiting for fully scaled quantum hardware.

Early Results and Pilot Scope

Initial simulation results, shared in late October 2022, demonstrated 8–12% efficiency gains in route scheduling under dynamic desert constraints compared with classical optimization baselines. Beyond efficiency, the simulations revealed improvements in logistics resilience, with more reliable refueling cadences and supply chain continuity even under terrain disruptions.

The partnership’s next milestone was set for mid-2023: live trials moving equipment between remote solar farms in the Al Dhafra region and Abu Dhabi’s coastal logistics hubs. Success here would validate the potential for scaling across Gulf-wide supply chains.

Technical Strategy: Hybrid Quantum-Classical Pipelines

Qubetron’s approach combines advanced machine learning with quantum-inspired solvers, creating a layered optimization framework:

1. State-of-the-art ML models predicted dynamic costs, travel times, and weather impacts.

2. Quantum-inspired optimizers tackled NP-hard VRP variants, simulating state transitions to explore broader solution spaces than classical heuristics.

3. Feedback loops integrated vibronics and telematics data from logistics vehicles, triggering re-optimization in real time during unexpected disruptions.

Critically, Qubetron’s platform did not require active quantum hardware. Instead, it simulated quantum effects on classical processors, making early pilots economically viable while laying a path to future quantum integration.

Strategic Regional Impacts

The ADIO-Qubetron partnership carries far-reaching implications:

• Middle East momentum: The UAE joins Saudi Arabia’s KAUST-Pasqal initiative in creating a regional hub for applied quantum logistics research.

• Cross-sector relevance: Beyond solar logistics, the same systems could serve oilfield resupply, desert mining operations, and humanitarian aid distribution in remote regions.

• Global standing: ADIO’s investment bolsters its image as a forward-thinking technology investor, signaling that quantum is not confined to laboratories but tested in real industry challenges.

For the Gulf region, where logistics underpins economic diversification, embedding quantum readiness could provide strategic advantage in global trade and sustainability.

Challenges and Future Plans

Despite promising results, the partnership acknowledged significant hurdles:

• Data modeling complexity: Capturing desert dynamics requires massive, granular datasets, and calibration remains an ongoing challenge.

• Hardware readiness: Full quantum hardware integration remains several years away, meaning near-term applications will rely on quantum-inspired approximations.

• Scaling across geographies: Extending pilots beyond Abu Dhabi to multi-region coordination will require deeper integration with airlines, ports, and trucking companies.

Future plans outlined by ADIO included expanding pilot routes, introducing broader asset classes like perishable goods, and formalizing academic partnerships with Khalifa University and other UAE research institutions.

Conclusion

The October 20, 2022 partnership between Abu Dhabi’s ADIO and Qubetron is more than a pilot—it represents a paradigm shift in how extreme-environment logistics may be managed. By tackling desert supply chains, one of the toughest logistical settings on Earth, the project demonstrates how quantum-inspired optimization can deliver measurable improvements today, while laying the groundwork for full quantum deployment in the future.

The initiative offers a vision of logistics that is more resilient, sustainable, and adaptive, with potential applications across sectors ranging from renewable energy supply chains to humanitarian relief operations.

If successful, the Abu Dhabi model could become a blueprint for quantum resilience worldwide, showing how even the harshest supply chain environments can benefit from the convergence of logistics expertise, advanced simulation, and quantum-inspired intelligence.

On October 17, 2022, IonQ, the U.S.-based leader in trapped-ion quantum computing, and Airbus, Europe’s largest aerospace manufacturer, jointly announced a landmark initiative to explore how quantum computing can optimize aircraft cargo loading. The project, officially titled Quantum Aircraft Loading Optimization & Quantum Machine Learning, is a 12-month pilot program designed to build and test algorithms that could reshape how cargo is planned, distributed, and managed in commercial aviation.

This marks one of the first times quantum computing has been directly applied to a live aerospace logistics problem. While much of quantum research has remained academic or confined to laboratory proofs of concept, Airbus’s willingness to invest in quantum optimization demonstrates both urgency and confidence in the potential of emerging technologies to meet aviation’s efficiency and sustainability challenges.

Quantum Meets Aircraft Loading Logistics

Aircraft cargo loading is not a simple matter of packing containers into a fuselage. Airlines and cargo carriers face a highly constrained optimization challenge. Each flight carries cargo of different sizes, weights, and priorities. Distribution must balance the aircraft’s center of gravity, adhere to safety standards, minimize loading time, and maximize payload utilization.

Traditionally, cargo planning relies on classical optimization techniques and heuristics, often producing acceptable—but not always optimal—results. When operating across thousands of flights, small inefficiencies accumulate into significant costs. Extra ground minutes translate into delayed departures. Suboptimal weight distribution leads to higher fuel burn. Missed opportunities for denser packing reduce payload revenue.

By framing cargo loading as a combinatorial optimization problem, IonQ and Airbus aim to leverage quantum algorithms to identify improved solutions faster. The expectation is not just marginal gains, but a systemic reshaping of how logistics teams plan, adapt, and execute loading in real-world conditions.

Why This Partnership Matters for Logistics

The Airbus–IonQ partnership signals three critical shifts in logistics technology:

1. Ground time reduction. Faster, more precise load planning means planes can spend less time at the gate, improving turnaround and network efficiency.

2. Fuel efficiency gains. Better distribution of weight reduces drag and improves fuel consumption—an increasingly vital metric as airlines pursue net-zero targets.

3. Payload maximization. Quantum-based algorithms could unlock new strategies for fitting more cargo without violating safety or balance rules.

Together, these improvements target both operational profitability and environmental sustainability. The October 2022 announcement effectively elevated quantum logistics from research speculation to commercial trial, suggesting that aerospace is emerging as a test bed for high-impact quantum applications.

Global Collaboration: U.S. Quantum Meets European Aerospace

The collaboration represents a transatlantic convergence of technological expertise and industry need.

• IonQ, headquartered in Maryland, USA, has established itself as a global leader in trapped-ion quantum computing. Its systems, such as IonQ Aria and the newer IonQ Forte, are accessible through major cloud platforms, making them usable for enterprise experiments without requiring dedicated on-premises hardware.

• Airbus, based in Europe, operates one of the most complex aerospace logistics networks in the world, spanning manufacturing, parts distribution, and global cargo operations.

By joining forces, the two companies bridge leading-edge quantum hardware with one of the toughest logistics use cases in the transportation sector.

Technical Strategy: Quantum Machine Learning for Cargo Loading

The initiative focuses on developing quantum machine learning (QML) algorithms tailored to the cargo loading problem.

Key technical pillars include:

• Problem mapping: Cargo assignment framed as an NP-hard combinatorial optimization challenge, translatable into binary decision variables.

• Quantum solvers: Early-stage quantum approaches—either annealing-style or gate-based circuits—will explore near-optimal loading strategies.

• Hybrid integration: Classical systems will preprocess and validate data, with the quantum system providing optimization guidance.

• Learning feedback loops: Quantum-enhanced algorithms will adapt through machine learning techniques, improving as more flight and cargo data are processed.

Though IonQ and Airbus expect initial quantum algorithms to complement rather than outperform classical solutions, the real-world testing will provide critical insights into scaling, latency, and feasibility for future operations.

Building on Airbus’s Quantum Track Record

Airbus has been steadily increasing its quantum footprint. Earlier in 2022, the company participated in QUASA (Quantum-enabled Services for Aerospace), exploring combinatorial optimization across aerospace logistics. Airbus has also invested in quantum key distribution and secure communications, alongside experimental design methods for future aircraft using quantum-inspired simulations.

This new project deepens Airbus’s strategy by shifting from theoretical research toward practical operational pilots—demonstrating its intent to transform logistics processes, not just prepare for long-term technological disruptions.

Pilot Scope, Timelines, and Deliverables

The IonQ–Airbus initiative is structured as a 12-month project with defined deliverables:

• Development of a prototype cargo-loading application by Q3 2023.

• Controlled real-world trials with Airbus logistics teams on selected aircraft fleets.

• Benchmarking studies comparing quantum-based approaches with classical load planning on criteria such as time-to-solution, payload utilization, and cost savings.

• Knowledge-sharing outputs, including research papers and seminars bridging aerospace and quantum communities.

The ambition is not only to prove feasibility but to set a foundation for enterprise-scale adoption of quantum logistics solutions.

Strategic and Environmental Value

Airlines and cargo operators are under mounting pressure to reduce costs and emissions simultaneously. If successful, the IonQ–Airbus pilot could offer a rare dual benefit:

• Operational cost savings through faster turnarounds and optimized payloads.

• Sustainability gains by reducing unnecessary fuel burn.

• Resilience and flexibility in responding to last-minute cargo changes or disruptions without major inefficiencies.

This aligns with broader industry shifts toward greener aviation and digital transformation in logistics.

Industry Ecosystem Response

The October 2022 announcement resonated across the logistics and quantum ecosystems.

• Air freight competitors and logistics software vendors signaled interest in parallel experiments.

• Quantum-South, a Uruguay-based startup, had already been applying IBM Quantum Network resources to cargo packing optimization, underscoring a growing ecosystem.

• Unisys and other technology firms also expanded logistics solutions incorporating quantum-inspired analytics earlier in 2022.

The IonQ–Airbus pilot thus fits into a larger wave of momentum pushing quantum into real-world logistics.

Challenges to Overcome

Despite optimism, technical and operational hurdles remain:

• Hardware limitations. Current quantum devices remain constrained in qubit count and error rates.

• System integration. Cargo-loading optimization must plug seamlessly into Airbus’s existing transport management systems.

• Algorithm maturity. Many optimization methods are still experimental and must be refined for noisy, near-term hardware.

• Proof of advantage. Convincing evidence of quantum outperforming or complementing classical baselines is crucial for adoption.

Overcoming these challenges will determine whether the project becomes a proof of concept or a step toward operational transformation.

What’s Next for Quantum Logistics

The roadmap points toward:

• Pilot execution in late 2022 and early 2023 with Airbus cargo operations.

• Scaling to broader logistics challenges, such as routing, scheduling, and warehouse slotting.

• Partnership expansion through Horizon Europe and collaborations with additional quantum hardware vendors.

• Ripple effects across aviation and shipping, encouraging more operators to explore quantum-enhanced tools.

If Airbus and IonQ can validate tangible efficiency gains, the project could catalyze an industry-wide shift toward quantum-ready logistics solutions.

Conclusion

The October 17, 2022 partnership between IonQ and Airbus represents a milestone in the convergence of quantum computing and aerospace logistics. By applying quantum machine learning to the notoriously complex challenge of aircraft cargo loading, the project offers a glimpse into how quantum technologies may soon enhance real-world supply chain operations.

More than a theoretical experiment, the initiative sets measurable targets—shorter turnaround times, improved payload efficiency, and lower environmental impact. As results unfold in 2023, the pilot will serve as both a technological test and a strategic signal: that quantum computing is moving out of the lab and into the logistics hubs where global commerce depends.

If successful, this collaboration could establish a blueprint for how quantum optimization reshapes transport and supply chain industries worldwide, advancing both economic performance and sustainability in tandem.

Ports sit at the heart of global trade, handling billions of tons of cargo each year. Yet behind the scenes, container yards face a difficult and often underestimated challenge: how to stack thousands of metal boxes efficiently. Poor container placement can mean extra moves for cranes, longer truck turnaround times, and increased dwell time for vessels—all of which translate to lost money and reduced throughput.

On October 5, 2022, D-Wave Systems, a pioneer in quantum computing, and Maersk, the world’s largest shipping company by container capacity, jointly announced a pilot project aimed at solving this problem. Conducted at Maersk’s Rotterdam terminal, one of the busiest ports in Europe, the project tested quantum annealing methods for optimizing container stacking. It marked the first time a major shipping line deployed quantum hardware in a commercial port environment, moving the technology from research discussions to operational pilots.

Port Efficiency: The Container Stacking Challenge

Container terminals must handle immense complexity. Each yard move involves choices that cascade through the rest of the operation: where to stack incoming containers, how to minimize reshuffling when a vessel departs, and how to balance the workload across multiple cranes. Traditionally, ports rely on heuristics—rules of thumb or classical optimization—to make decisions. These methods can work, but they often produce sub-optimal results, especially under high traffic or disruption scenarios.

The October 5 pilot tackled this by reformulating the stacking challenge into a Quadratic Unconstrained Binary Optimization (QUBO) model. QUBOs are mathematical representations that quantum annealers like D-Wave’s Advantage system can process efficiently. By translating crane schedules and yard layouts into binary variables, the pilot enabled the quantum system to search for container arrangements that reduce unnecessary moves and idle time.

Early results were encouraging: simulations showed up to a 15% reduction in crane idle time and a 10% decrease in container handling moves. These numbers, while modest on paper, could translate into millions of dollars in annual savings and improved reliability for global trade.

Why This Pilot Matters

While quantum applications in finance, drug discovery, and machine learning often attract headlines, logistics and supply chain operations face some of the hardest computational problems in practice. Container stacking is a textbook case: the problem grows exponentially with each added variable. For ports moving millions of containers annually, even a 5% efficiency gain could yield enormous economic benefits.

By piloting at Europe’s largest port, Maersk and D-Wave demonstrated that quantum optimization is no longer confined to academic experiments or synthetic datasets. Instead, it can integrate directly with live Terminal Operating Systems (TOS) and provide actionable insights for crane operators and planners.

Technical Framework & Operational Setup

The collaboration built a structured technical pipeline to translate logistics problems into quantum-ready form.

• QUBO Modeling: Each possible container position in the yard was represented as a binary decision variable. Constraints such as container weight, hazardous cargo regulations, and departure schedules were encoded into the QUBO framework.

• Annealing Runs: The D-Wave Advantage system, accessed through cloud infrastructure, processed thousands of QUBO instances per run. These annealing cycles searched for container layouts that minimized handling moves while keeping operations flexible.

• Feedback Integration: Pilot results were integrated into Maersk’s TOS as alternative planning schedules. Operators could compare classical outputs with quantum-optimized proposals, allowing side-by-side benchmarking.

• Visualization Tools: Engineers created dashboards that mapped proposed stacking solutions to yard diagrams, making it easier for human decision-makers to interpret and adopt results.

This hybrid setup reflected a practical understanding: quantum annealing on its own was not the entire solution, but when paired with classical systems and operator expertise, it could unlock measurable improvements.

Initial Outcomes and Future Steps

The October pilot produced several tangible outcomes:

• 10% reduction in container handling moves: Fewer unnecessary re-stacks saved fuel, time, and labor.

• 15% decrease in crane idle time: More consistent crane assignments kept equipment active and productive.

• 5–7% projected lift in daily throughput: Simulations showed how even marginal gains could scale significantly across a busy terminal.

Building on these results, the team planned live trials with larger batch sizes—testing optimization across 50+ containers per run—and exploring applications for multi-terminal coordination, particularly across Maersk’s European hubs.

Broader Industry Response

News of the Maersk–D-Wave pilot quickly rippled through the shipping and port industries.

• Shipping Lines: Competitors like MSC and CMA CGM began evaluating quantum feasibility studies for their own terminals.

• Port Authorities: The Port of Hamburg and PSA International in Singapore initiated research discussions about adopting quantum-enhanced logistics.

• Technology Providers: Logistics software firms such as Navis and Kalmar explored hybrid algorithms that integrate classical scheduling with quantum optimization modules.

This response underscores how quickly quantum technologies are moving from proof-of-concept to industry attention, especially in sectors where even small efficiency gains drive significant financial and environmental impact.

Challenges to Overcome

Despite the positive outcomes, several hurdles remain before quantum annealing can become a standard feature in port operations:

1. Hardware Availability – Access to quantum annealers remains limited, often cloud-based, raising questions about latency and reliability in mission-critical environments.

2. Scalability – As container yard size increases, so too does the complexity of QUBO problems. Expanding capacity without losing performance remains a key technical challenge.

3. Integration – Outputs must flow seamlessly into live TOS platforms, with user-friendly interfaces that port operators can trust and act upon.

4. Environmental Noise – Operational variability—weather, labor strikes, or sudden surges in container volume—can make optimization results harder to implement consistently.

Addressing these factors will determine whether pilots like Rotterdam’s scale into permanent adoption.

Towards a Quantum-Enabled Port Future

Looking ahead, Maersk and D-Wave outlined several next steps:

• Expansion to Antwerp and Singapore in 2023, testing quantum annealing in diverse port environments.

• Solver Enhancements, with D-Wave working on larger, more robust QUBO formulations capable of handling thousands of variables.

• Multi-Site Logistics, enabling optimization across multiple terminals simultaneously—a capability critical for integrated shipping networks.

If successful, these developments could fundamentally reshape how container yards operate worldwide, creating smarter, leaner, and more sustainable logistics systems.

Conclusion

The October 5, 2022 announcement of the Maersk–D-Wave pilot marked a pivotal step in the evolution of port logistics. By applying quantum annealing to container stacking, the project moved beyond theory, delivering measurable improvements in crane utilization and container handling efficiency.

The results show that quantum technologies are no longer distant possibilities—they are becoming practical tools for solving some of the hardest optimization problems in global trade. As Maersk, D-Wave, and other stakeholders scale pilots across more ports in 2023 and beyond, the vision of quantum-enabled logistics may soon become a cornerstone of maritime operations.

For ports facing growing cargo volumes, climate challenges, and economic pressures, the promise of quantum is clear: more efficient stacking, faster turnaround, and resilient global supply chains.

In September 2022, the University of South Carolina (USC), in collaboration with the Port of Charleston and several emerging quantum-technology firms, launched one of the first U.S. pilots to apply quantum-inspired algorithms to container logistics. The trial was designed to explore how hybrid computational systems could optimize gate appointment scheduling, container yard stacking, and real-time operations management in one of the busiest ports on the U.S. East Coast.

The move came at a pivotal time. By late 2022, U.S. ports were facing immense logistical pressures: truck congestion, long dwell times, and container backlogs. Traditional logistics software, while robust, was showing limitations in responding dynamically to disruptions such as labor shortages, weather events, and shifting global supply chain demands. Against this backdrop, USC’s researchers argued that quantum-inspired optimization could provide fresh tools to increase throughput and reduce inefficiencies.

Why U.S. Ports Are Turning to Quantum-Inspired Tools

The motivation for the Charleston pilot was grounded in economic and operational urgency. Studies conducted by USC earlier that year estimated that applying advanced optimization tools in freight logistics could unlock up to $8.5 billion annually in additional economic output for South Carolina alone. Much of this potential gain lay in reducing wasted time and resources at ports—particularly by addressing truck queuing and yard inefficiencies.

Classical optimization software is limited in how quickly it can adapt to complex, multi-variable scheduling problems. Gate appointments must be matched with crane availability, berth allocations, and yard stacking conditions—all while accounting for disruptions. Quantum-inspired systems offer new approaches by combining reinforcement learning, probabilistic models, and heuristic searches designed to mimic the parallel exploration of quantum systems.

The Port of Charleston, already one of the nation’s top container hubs, became the natural testbed. The pilot aimed to prove that such tools could work in live operations without requiring fully developed quantum computers, which remain in early stages of practical deployment.

Pilot Scope and Strategic Objectives

The September pilot focused on three core operational challenges:

1. Gate Appointment Optimization – Scheduling truck arrivals in ways that would minimize bottlenecks and align yard resources efficiently.

2. Container Stacking Coordination – Optimizing placement in the yard to reduce unnecessary re-handling and speed up crane operations.

3. Real-Time Reoptimization – Allowing dynamic adjustments as conditions changed, whether due to weather delays, traffic surges, or unexpected vessel schedules.

Live operational data from the port was streamed into a hybrid optimization engine developed by USC researchers and local technology partners. This engine then generated recommendations that were tested against existing terminal operating system (TOS) benchmarks through A/B trials.

Technical Architecture: Hybrid and Quantum-Inspired

The system was not dependent on quantum hardware. Instead, it relied on what USC termed a quantum-inspired architecture, which blended classical machine learning with heuristic methods modeled on quantum principles.

Key components included:

• Reinforcement Learning Agents trained on historical port operation data, learning to predict optimal gate assignments.

• Heuristic Modules designed to explore multiple scheduling solutions simultaneously, inspired by concepts of quantum superposition.

• Feedback Loops where model recommendations were reviewed by human operators, creating iterative refinements and ensuring practical alignment.

This design represented a pragmatic middle ground: it leveraged ideas from quantum computing without requiring access to quantum machines, which remain scarce and expensive.

Pilot Outcomes: Tangible Throughput Improvements

By the end of the September trial, results demonstrated meaningful improvements across several metrics:

• 12% reduction in yard dwell time, easing container congestion.

• 10% reduction in truck wait times, improving efficiency for carriers.

• 8% improvement in yard slot utilization, enabling better use of available space.

• Reaction windows reduced to 5 minutes, enabling planners to respond faster to disruptions.

These outcomes confirmed that hybrid, quantum-inspired tools could deliver practical operational value even before the arrival of large-scale quantum computing.

Comparison to Pure Quantum Pilots

Other ports, such as Los Angeles, were experimenting directly with quantum annealers like those developed by D-Wave to optimize stacking operations. Charleston’s project, by contrast, showcased a different pathway: using quantum-inspired methods that run on classical hardware but apply novel optimization strategies.

This approach aligns with the broader U.S. strategy under the CHIPS and Science Act, which emphasizes applied research for infrastructure modernization and resilience. By proving that such tools could work in current environments, USC and Charleston positioned themselves as leaders in practical quantum adoption.

Global and Regional Ecosystem Response

The pilot attracted attention far beyond South Carolina. Other U.S. ports, including Savannah, Brunswick, and Tacoma, expressed interest in similar trials. Canada’s Vancouver Port Authority also monitored the results closely, viewing Charleston as a model for North American adoption.

Technology firms specializing in logistics and optimization—including Zapata, QC Ware, and Multiverse Computing—took note, evaluating how their own platforms could integrate quantum-inspired methods. Academic recognition also followed, with USC’s newly established Center for Quantum Logistics being highlighted as a hub for advancing this field.

Integration Hurdles and Lessons Learned

While the trial was successful, several challenges emerged:

• Data Latency – Integrating real-time feeds required stronger data infrastructure.

• Model Explainability – Operators needed dashboards that clearly explained algorithmic recommendations.

• Scalability – Expanding from pilot scale to full port operations would require more computing power and systems integration.

• Workforce Training – Port personnel needed new skills to work with hybrid analytics tools.

These lessons underscored that technology adoption must be paired with human-centered design and organizational adaptation.

Path to Scaling Up

Following the September pilot, stakeholders set a roadmap for expansion:

• New Modules – Adding berth assignment and crane sequencing optimization by early 2023.

• Infrastructure Investment – Building stronger telemetry links between port equipment and optimization engines.

• Benchmarking – Sharing pilot data with other ports to compare results and foster wider adoption.

• Federal Collaboration – Seeking U.S. Department of Transportation funding to support scaling across regional ports.

Alignment with National Policy

The timing of the pilot was strategic. In 2022, the U.S. federal government emphasized the modernization of supply chains through advanced technology adoption. The Infrastructure Investment and Jobs Act, combined with CHIPS and Science Act funding, created financial and policy support for innovative port technologies.

Private-sector players also responded. Logistics software providers such as Navis and Kalmar began building quantum-ready modules into their 2023 product roadmaps, influenced by Charleston’s pilot results.

Strategic Implications for Logistics

For global supply chain networks, the implications of the Charleston pilot are significant:

• First-Mover Advantage – Ports that adopt hybrid optimization early gain competitive edge in efficiency and sustainability.

• Intermodal Benefits – Improvements at ports directly translate into gains for rail and trucking partners.

• Resilience – Adaptive models help maintain throughput under disruptions, from strikes to storms.

By embedding quantum-inspired optimization into daily port operations, logistics networks can prepare for an era of greater complexity and volatility.

Future Outlook and Economic Impacts

Looking ahead, USC and its partners have outlined several priorities:

• Testing Quantum Coprocessors to accelerate specific optimization routines.

• Developing Cross-Port Systems that link Charleston’s optimization suite with other regional hubs like Savannah and Jacksonville.

• Launching Workforce Programs to train logistics engineers in hybrid-quantum tools.

• Driving Economic Growth, with projections suggesting billions in added output for South Carolina if such systems scale.

Conclusion

The September 21, 2022 pilot at the Port of Charleston represents a turning point in the integration of quantum-inspired methods into U.S. logistics. By applying hybrid optimization to real-world port operations, USC and its partners demonstrated measurable gains in efficiency, throughput, and resilience.

As ports worldwide search for solutions to congestion and supply chain instability, Charleston’s example shows how quantum-inspired systems can deliver practical value today while laying the foundation for future quantum computing applications. The initiative not only strengthens South Carolina’s economy but also positions the U.S. as a leader in the emerging field of quantum-enhanced logistics—ensuring that supply chains remain agile and secure in an increasingly complex global environment.

A New Quantum Algorithm for Supply Chain Management

On September 17, 2022, researchers Hansheng Jiang, Zuo-Jun Max Shen, and Junyu Liu introduced a groundbreaking approach to supply chain optimization with the release of their paper, Quantum Computing Methods for Supply Chain Management. Their work focused on one of the hardest challenges in operations research: dynamic inventory control under uncertain demand.

Traditional inventory management requires exploring enormous state-action spaces, especially in global supply chains where replenishment decisions ripple across multiple warehouses, suppliers, and distribution hubs. Classical policy iteration—an iterative method of dynamic programming—has long been applied to such problems but often becomes computationally intractable at scale. Jiang and his colleagues proposed embedding quantum subroutines into the policy iteration process, thereby accelerating convergence and enabling decision-making across larger, more complex supply chain environments.

The core innovation—“quantized policy iteration”—demonstrates how hybrid models can already provide benefit on NISQ (Noisy Intermediate-Scale Quantum) devices, long before fault-tolerant quantum hardware arrives.

Methodology: Bridging Quantum and Operations Research

The team’s research was structured around adapting well-known logistics models into a quantum-compatible framework.

• Inventory Models: They began with stochastic inventory replenishment models, accounting for uncertain demand fluctuations.

• Policy Iteration Enhancement: Traditional policy iteration involves repeated evaluation and improvement of decision policies. The researchers replaced the evaluation step with quantum-enhanced algorithms, allowing them to scan large state-action spaces more efficiently.

• Quantum Simulation: Experiments ran on IBM’s Qiskit simulator and the qBraid platform. While not yet on large quantum processors, the simulations highlighted meaningful speedups in policy convergence for small-scale environments.

• State-Space Encoding: Quantum registers were used to encode state distributions, compactly representing large amounts of information that classical methods struggle with.

This approach is important because it moves beyond simple routing problems, which have dominated quantum logistics research, into the heart of supply chain management—inventory control and restocking decisions.

Logistics Implications: Toward Quantum-Ready Operations

Inventory control is at the center of logistics efficiency. From manufacturing plants to distribution centers, effective policies for restocking and replenishment ensure that companies avoid costly shortages while minimizing excess storage. Jiang and his colleagues demonstrated that quantum-enhanced policy iteration could yield:

• Faster convergence to optimal inventory policies.

• More adaptive decision models under uncertainty.

• Practical frameworks that can be hybridized with existing enterprise systems.

In dynamic, multi-echelon supply chains—where one warehouse’s inventory decision impacts upstream suppliers and downstream retailers—such improvements are transformative. Even modest algorithmic gains translate into reduced costs, fewer delays, and smoother operations across the logistics ecosystem.

Global Relevance and Industrial Momentum

Although published as academic research, the September 2022 paper quickly captured industry attention. Supply chain consultancies and logistics software vendors began referencing it as an early signal of how quantum tools could reshape future planning systems.

IBM’s earlier work in 2022 on quantum logistics emphasized the need for integrated routing and inventory optimization. Jiang et al.’s contribution provided the theoretical foundation for this integration, showing how inventory policies could be quantum-enhanced.

Meanwhile, companies such as D-Wave, Multiverse Computing, and Zapata Computing began exploring similar hybrid models for inventory planning and restocking simulations. Industry uptake underscored a growing recognition: routing problems alone are insufficient—supply chain resilience also requires smarter inventory decision-making.

Technical Innovations Explained

The research’s novelty rested on three main technical contributions:

1. Quantized Policy Iteration
   Each cycle of policy improvement was embedded with quantum-enhanced evaluations. This allowed for faster scanning of state-action outcomes, particularly under stochastic demand.

2. Hybrid Architecture
   The loop itself remained classical, ensuring reliability, while quantum subcircuits delivered speed in the most computationally demanding segments.

3. Compact State-Space Representation
   Quantum registers encoded large decision spaces efficiently, permitting exploration of dimensions beyond classical feasibility.

Together, these created an algorithmic framework that can adapt as quantum hardware matures, scaling from simulation today to live systems in the near future.

Academic and Industry Uptake

The study’s release was followed by tangible recognition in both academic and professional circles. Industry surveys indicated rising quantum adoption in logistics, often citing Jiang et al.’s framework as an example of how early-stage algorithms could influence planning.

European and U.S. research labs began incorporating quantized control methods into broader programs on quantum reinforcement learning. Simultaneously, IBM highlighted the approach as a stepping stone toward end-to-end logistics modeling, including routing, disruption recovery, and inventory management.

This uptake illustrates the growing feedback loop between academic innovation and industrial application, accelerating the quantum logistics roadmap.

Practical Use Cases: Where Quantum Meets Logistics Control

The potential applications of quantized policy iteration extend across multiple industries:

• Retail Distribution: Optimizing reorder points and quantities across nationwide or global distribution networks.

• Manufacturing: Supporting just-in-time production systems by dynamically adjusting inventory to match unpredictable demand.

• Port Logistics: Coordinating inbound shipment arrivals with inventory replenishment cycles in intermodal transport systems.

In all cases, the ability to refine policies quickly under uncertainty is a decisive advantage—especially as supply chains face increasing volatility from geopolitical, environmental, and consumer demand shifts.

Challenges and Hardware Road Map

Despite the promise, challenges remain. Current quantum processors have limited qubit counts and shallow circuit depth, constraining the size of real-world problems they can handle. Scaling from small inventory models to full enterprise datasets requires algorithmic adaptation and stronger error mitigation.

Integration is another hurdle. Quantum outputs must flow seamlessly into enterprise platforms such as ERP and warehouse management systems. Building middleware to bridge these domains will be critical.

Nevertheless, hardware development continues apace. As devices reach 100+ qubits with lower noise, hybrid approaches like quantized policy iteration will become increasingly practical, paving the way for pilot projects on industry-relevant scales.

Next Steps and Research Outlook

Looking forward, the roadmap includes:

• Pilot Collaborations: Partnerships between logistics firms and academic institutions to test quantized algorithms in simulated and semi-operational environments.

• Benchmarking: Establishing standardized datasets and metrics to measure quantum advantage against classical baselines.

• Hybrid Integration: Merging IBM Qiskit with leading supply chain software platforms such as SAP or Blue Yonder.

• Public Funding: European and U.S. initiatives under programs like Horizon Europe may soon support real-world supply chain pilots using quantum algorithms.

These steps indicate a trajectory toward practical deployment, moving from academic blueprint to industry implementation.

Strategic Significance

The September 17, 2022 publication stands as a milestone in quantum logistics. It marked the first significant step beyond routing into the deeper mechanics of supply chain optimization. By proving that hybrid quantum-classical approaches could accelerate policy iteration even at small scales, Jiang, Shen, and Liu positioned quantum logistics as an imminent, not distant, frontier.

For supply chain leaders, this signals the need to prepare for quantum-readiness now—investing in skills, partnerships, and data pipelines that will enable them to capitalize on emerging computational capabilities.

Conclusion

The quantized policy iteration algorithm introduced by Jiang, Shen, and Liu on September 17, 2022 represents a turning point for quantum logistics research. By embedding quantum subroutines into classical policy iteration, the team demonstrated how inventory control models—core to global supply chain operations—can be enhanced with quantum computation.

Though hardware limitations remain, the framework establishes a foundation for industry adoption. The research shows that hybrid quantum approaches are no longer theoretical curiosities but emerging tools with measurable impact. As logistics networks become more complex and demand more resilient decision-making, the ability to harness quantum algorithms for faster, smarter inventory policies may soon become a strategic differentiator.

In effect, this work provides the academic blueprint for future supply chain optimization—bringing quantum logistics one step closer to practical, global application.

A Landmark Quantum Alliance in Asia-Pacific

The announcement on September 10, 2022, positioned both Quantinuum and Mitsui & Co. at the forefront of quantum-enabled logistics. For Quantinuum, the world’s largest standalone quantum computing company, the deal followed a technical breakthrough: its System Model H-1 trapped-ion quantum computer achieved a quantum volume (QV) of 8192, then the highest benchmark in the industry. This achievement signaled readiness to transition quantum applications from experimental phases into operational pilot programs.

For Mitsui, one of Japan’s largest trading and logistics conglomerates, the partnership provided an opportunity to apply cutting-edge quantum technology directly to its sprawling supply chain and logistics networks. By linking Quantinuum’s hardware and software expertise with Mitsui’s global logistics infrastructure, the alliance established a foundation for real-world testing of quantum optimization in freight, inventory, and port operations.

Quantum Volume and Why It Matters

The concept of quantum volume (QV) is critical in measuring the performance of quantum computers. Unlike simple qubit counts, QV captures both the number of qubits and their error rates, reflecting the system’s ability to execute deep, complex circuits reliably. With a QV of 8192, Quantinuum’s System Model H-1 demonstrated a significant leap in computational robustness.

For logistics, this metric matters because supply chain optimization problems—such as routing thousands of shipments through multiple ports, balancing warehouse inventory, or coordinating intermodal transport—require deep circuit executions. Previous generations of quantum devices often struggled to sustain accuracy across such large problem spaces. The QV8192 milestone gave both partners confidence that the H-1 could begin delivering tangible benefits to real industrial challenges.

Mitsui: A Strategic Player in Global Logistics

Mitsui & Co. operates across 63 countries and manages a vast portfolio that spans energy, machinery, chemicals, and food industries. Within logistics, Mitsui’s footprint is expansive:

• Freight forwarding for global trade routes.

• Port terminal operations across Asia and beyond.

• Warehouse and inventory management for diverse sectors.

• Intermodal transport coordination integrating trucks, rail, and shipping.

This depth and scale make Mitsui an ideal partner for exploring quantum optimization pilots. Its daily operations involve millions of data points, schedules, and routes, all of which provide fertile ground for testing quantum approaches that aim to reduce costs, improve efficiency, and enhance sustainability.

Areas of Joint Development and Use Cases

The Quantinuum-Mitsui alliance outlined several priority use cases for quantum-enhanced logistics:

1. Freight Routing and Port Scheduling
   Quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) will be applied to optimize truck, rail, and ship scheduling. This is particularly relevant for congested port hubs, where delays often cascade through global supply chains.

2. Inventory Distribution
   By using stochastic optimization and hybrid classical-quantum models, Mitsui aims to better allocate inventory across regional warehouses. This could minimize shipping distances, reduce costs, and improve service levels.

3. Demand Forecasting Integration
   Quantinuum’s Quantum Monte Carlo Integration (QMCI) engine will be leveraged for probabilistic demand forecasting, helping logistics planners anticipate fluctuations in orders and adapt distribution strategies accordingly.

4. Hybrid Cloud and Quantum Infrastructure
   The alliance envisions cloud-based access to Quantinuum’s H-1 system, paired with real-time data feeds from Mitsui’s ports and warehouses. This hybrid setup will enable localized pilots that directly reflect Asia-Pacific logistics challenges.

Pilot projects were scheduled to begin in late 2022, with early results expected in 2023.

Regional and National Implications in Japan

The partnership aligns with Japan’s National Quantum Strategy, which emphasizes collaboration between government, industry, and academia to advance quantum applications. By anchoring pilots in actual supply chain environments, Mitsui is moving beyond theoretical exploration into operational testing.

The implications are significant: if successful, quantum-enabled logistics could enhance Japan’s competitiveness as a regional trade hub while also supporting resilience against global disruptions, such as pandemic-related bottlenecks or geopolitical tensions affecting shipping routes.

Enabling Hybrid Optimization via System Model H-1

Quantinuum’s System Model H-1 is based on trapped-ion technology, renowned for high coherence and low error rates. Its architecture supports complex hybrid algorithms that combine classical heuristics with quantum subroutines.

Potential approaches include:

• QAOA for routing and scheduling optimization.

• Quantum Monte Carlo methods for probabilistic demand modeling.

• Variational hybrid techniques for adaptive resource planning.

The integration of these algorithms with Mitsui’s logistics datasets offers the possibility of breakthroughs in efficiency and decision-making accuracy, well beyond what classical systems can achieve alone.

Broader Ecosystem Momentum

The Quantinuum-Mitsui announcement resonated across the Asia-Pacific and global logistics sectors. Other major players, including Mitsubishi and international logistics firms, began exploring similar pilots. Meanwhile, governments in South Korea and Australia expressed interest in regional platforms for quantum transport optimization.

Academic institutions, such as RIKEN and the University of Tokyo, also announced exploratory programs to study quantum supply chain applications, often in collaboration with industry partners. The alliance thus catalyzed a broader ecosystem shift toward quantum-enabled logistics innovation in Asia.

Challenges and Strategic Considerations

Despite optimism, the partnership faces several challenges:

• Integration with existing systems: Logistics runs on complex software platforms (ERP, TMS, WMS). Quantum outputs must be adapted seamlessly.

• Talent and culture: Training frontline logistics teams to use quantum-augmented tools will require significant effort.

• Scalability: Ensuring algorithms that work on small pilots also scale effectively to nationwide logistics networks.

• Governance and data privacy: Handling sensitive supply chain data securely while ensuring compliance across multiple jurisdictions.

Addressing these challenges will be critical for the alliance’s success.

Environmental and Efficiency Benefits

Optimized logistics not only reduce costs but also improve sustainability outcomes. Benefits include:

• Lower fuel consumption through smarter routing.

• Reduced idle time at ports and warehouses.

• Higher load efficiency, reducing empty return trips.

• Compact inventory strategies that cut warehousing carbon footprints.

Given the Asia-Pacific’s focus on decarbonization and energy transition, these outcomes strengthen the business case for quantum adoption in logistics.

Strategic Outlook and Expansion Roadmap

The roadmap for the partnership includes:

• Publishing pilot results in 2023, showcasing measurable savings and efficiencies.

• Expanding deployment from Mitsui’s Japan hubs into Southeast Asia.

• Developing shared platforms and application libraries for industry-wide adoption.

• Supporting Japan’s role in international quantum innovation initiatives.

Success in logistics could also pave the way for Mitsui to explore quantum applications in its other sectors, including energy trading, finance, and manufacturing.

Significance in the Global Quantum-Logistics Landscape

This partnership is among the earliest commercial initiatives to embed quantum computing in real-world logistics operations in Asia. It represents a turning point: moving beyond simulations into live pilot deployments. By combining Quantinuum’s technical progress with Mitsui’s logistics expertise, the alliance demonstrates how quantum computing can address complex, data-heavy challenges with real economic impact.

Conclusion

The Quantinuum-Mitsui alliance, announced on September 10, 2022, represents a pivotal moment in the journey toward quantum-enhanced logistics. By merging world-leading quantum hardware with one of the globe’s most sophisticated logistics networks, the partnership is setting the stage for real-world optimization pilots that could transform supply chain efficiency, resilience, and sustainability. If successful, it will not only reshape operations within Japan and the Asia-Pacific but also provide a model for global adoption, proving that quantum computing is ready to move from promise to practice in the logistics sector.

The global logistics sector entered 2022 under immense strain. Ongoing supply chain disruptions, container shortages, and record congestion at major ports highlighted the need for operational innovation. Against this backdrop, a breakthrough collaboration unfolded at the Port of Los Angeles. On September 5, 2022, D-Wave Systems, in partnership with SavantX, launched a commercial-scale pilot demonstrating how quantum annealing can optimize container stacking and yard operations in one of the world’s busiest shipping hubs.

This pilot, conducted at Pier 300 in Los Angeles, signaled more than a technical experiment. It represented a shift in how logistics operators might use next-generation computation to handle rising cargo volumes with greater efficiency and reduced environmental cost.

Port Operations Under Optimization Pressure

The Port of Los Angeles serves as the busiest container port in the United States, moving millions of TEUs (twenty-foot equivalent units) annually. In such an environment, even minor inefficiencies cascade into significant consequences. A single misplaced container can ripple through crane schedules, delay truck appointments, and extend vessel turnaround times.

By 2022, bottlenecks had grown so severe that vessel wait times extended into weeks during peak congestion. Trucks queued for hours, wasting fuel, while cranes operated below maximum efficiency. The port, like many around the world, recognized that classical optimization techniques were insufficient to keep pace with dynamic, large-scale scheduling demands.

Container stacking in particular stood out as a pain point. Decisions about where to place containers in the yard directly affect crane movements, truck dwell times, and overall throughput. Traditional heuristic systems provided workable but suboptimal solutions. The question became: could quantum computing do better?

The SavantX–D-Wave Annealing Pilot

SavantX, a Santa Fe-based technology company, had already developed its HONE (Hyper Optimization Nodal Efficiency) platform to address port inefficiencies. In September 2022, the company partnered with D-Wave to apply quantum annealing to container stacking at the Port of Los Angeles.

At the technical core of the pilot was the QUBO (Quadratic Unconstrained Binary Optimization) formulation. Each container placement and crane assignment was represented as a set of binary variables. The quantum annealer, hosted via D-Wave’s Advantage system, processed thousands of possible yard configurations in parallel to identify those minimizing unnecessary moves and aligning container positions with truck and vessel schedules.

The pilot workflow followed a clear sequence:

1. Data ingestion – Real-time truck schedules, vessel manifests, and yard layouts were fed into the system.

2. QUBO formulation – Optimization problems were modeled using binary encodings of container placements.

3. Quantum annealing runs – The D-Wave Advantage annealer generated candidate stacking solutions.

4. Integration with terminal systems – Optimized results were displayed on operator dashboards for human review and approval.

5. Feedback cycles – Yard operations fed performance back into the model, allowing re-optimization every hour.

This closed loop represented one of the first large-scale integrations of quantum optimization into a live U.S. container terminal.

Outcomes: Measurable Operational Improvements

Early results from the pilot exceeded expectations. At Pier 300, the system delivered:

• 62% increase in crane deliveries per day compared to baseline operations.

• Reduction of truck wait times by approximately 10 minutes per visit, a significant improvement when multiplied across thousands of daily appointments.

• Greater yard turnover and crane efficiency, reducing congestion and accelerating vessel service.

Though framed as a pilot, these results suggested that quantum annealing could outperform both heuristic algorithms and manual scheduling practices. For an industry accustomed to incremental efficiency gains, such performance improvements were striking.

Why Critical Timing Matters

The timing of this pilot carried weight. By late 2022, supply chains were still recovering from the COVID-19 pandemic, labor shortages, and geopolitical disruptions. Port congestion was not merely a U.S. issue—it was global, affecting everything from electronics to food distribution.

While companies like IBM, Volkswagen, and Airbus had explored theoretical logistics use cases for quantum computing, D-Wave’s work at Los Angeles was among the first to yield measured, operational outcomes in a live shipping terminal. The ability to point to concrete metrics like throughput gains and reduced dwell times elevated quantum logistics from concept to commercial reality.

Global Ripples & Industry Adoption

News of the pilot reverberated across the maritime industry. Within months:

• Port authorities in Rotterdam and Hamburg launched feasibility studies based on similar container stacking models.

• Global carriers including MSC and CMA CGM began examining whether quantum optimization could improve turnaround times across their networks.

• Vendors such as Navis and Kalmar expressed interest in embedding quantum optimization APIs into their software and crane automation platforms.

The Los Angeles pilot effectively demonstrated that quantum annealing could slot into existing workflows without requiring wholesale infrastructure replacement—an essential factor in encouraging adoption.

Limitations & Scalability Challenges

Despite promising data, the pilot also revealed constraints.

• Hardware access remains an issue, as D-Wave’s Advantage system is primarily cloud-based, introducing latency compared to on-premise deployments.

• Problem size is bounded by the number of qubits, limiting the scale of optimization batches.

• Operational variability such as weather, labor shortages, or equipment downtime introduces factors that optimization models cannot fully anticipate.

• Human trust in algorithmic decision-making remains a hurdle, with operators reluctant to fully delegate critical yard operations.

These challenges underscore that quantum logistics is still in early development. Scaling from pilot to full-scale deployment requires technical, organizational, and cultural adaptation.

Roadmap for Broader Adoption

Looking ahead from September 2022, several initiatives were set in motion:

• Multi-terminal pilots in Antwerp and Singapore testing QUBO stacking logic across 100+ containers per batch.

• Refined solver performance through improved annealing schedules and enhanced constraint modeling.

• Expansion to multimodal logistics, integrating container stacking with truck routing and rail sequencing.

• Standards development via industry working groups aiming to define benchmarks for quantum logistics optimization.

These roadmaps reflected growing confidence that quantum tools could move from research pilots to enterprise platforms within the next few years.

Ecosystem Reinforcement: Quantum-Logistics Alignment

Industry discourse in September 2022 reinforced the importance of the Los Angeles trial.

• IBM’s logistics report (August 2022) identified port scheduling as a prime early-stage quantum use case.

• A Zapata-led survey published September 30, 2022 reported that 63% of logistics companies were already exploring quantum pilots, with ports and distribution centers at the forefront.

• SupplyChainBrain and other trade outlets highlighted D-Wave’s work as a leading example of real-world annealing application.

The convergence of pilot success and market readiness created a fertile environment for broader adoption.

Strategic Significance for Global Logistics

The Port of Los Angeles pilot demonstrated quantum’s value along multiple vectors:

• Commercial viability – Data-backed results encouraged industry confidence.

• Environmental sustainability – Reduced crane moves and idle times directly lowered emissions.

• Operational resilience – Real-time re-optimization improved adaptability to congestion and disruptions.

• Competitive advantage – Early adopters could position themselves as leaders in logistics innovation.

Such outcomes elevated quantum annealing from a promising theory to a tangible differentiator in global supply chains.

Conclusion

The September 5, 2022 pilot at the Port of Los Angeles stands as a landmark in the evolution of quantum logistics. By pairing D-Wave’s annealing technology with SavantX’s HONE platform, the project delivered measurable throughput gains in one of the most complex logistics environments on earth.

While challenges remain in scaling, trust, and hardware access, the pilot proved that quantum annealing can deliver operational value where classical methods struggle. For an industry under immense pressure to move goods faster, greener, and more efficiently, the experiment provided both a proof-of-concept and a roadmap.

As other ports and carriers explore quantum optimization, the Los Angeles trial may be remembered as the inflection point when quantum computing crossed from potential to practice in global logistics. Container stacking—once seen as a routine yard management task—has become the proving ground for one of the world’s most advanced computational technologies.

Safeguarding Logistics in a Post-Quantum Era

The logistics industry stands at the crossroads of rapid digitalization and unprecedented cyber risk. Every day, global supply chains rely on billions of IoT devices—tracking cargo via RFID, coordinating port cranes, managing warehouse climate controls, and directing fleets of automated guided vehicles (AGVs). These systems are increasingly interconnected, efficient, and indispensable. Yet they are also vulnerable.

As quantum computing matures, cryptographic protocols that have long safeguarded digital infrastructure—RSA, elliptic-curve cryptography (ECC), and related standards—face obsolescence. Quantum algorithms such as Shor’s could, in principle, render these schemes breakable in hours or minutes, jeopardizing the trust backbone of IoT networks. For logistics, where downtime translates directly to economic loss and bottlenecks ripple across global trade, this is an existential challenge.

Recognizing this urgency, Honeywell announced on August 30, 2022, a pioneering cybersecurity trial in Singapore. The initiative deployed post-quantum cryptography (PQC) within live logistics environments, safeguarding IoT-driven operations in one of Asia’s busiest trade hubs. The trial represents one of the first attempts to merge industrial IoT infrastructure with lattice-based PQC algorithms in production-like conditions.

Trial Scope and Partners

The pilot was carried out at a major port-adjacent warehouse facility in Singapore—a strategic choice, given the city-state’s role as a transshipment nexus and technology adoption leader. Honeywell partnered with SecureAsia, a local cybersecurity firm, and worked alongside national research centers to ensure compliance with emerging PQC standards.

The trial focused on integrating quantum-resistant encryption into three critical logistics domains:

• RFID-based inventory systems that track cargo movements at scale.

• Automated guided vehicles (AGVs) responsible for warehouse floor traffic.

• Cold-chain management sensors, monitoring temperature-sensitive goods such as pharmaceuticals and perishables.

Together, these systems represented hundreds of IoT endpoints, from handheld scanners to embedded warehouse sensors. For each, Honeywell implemented dual-stack communication protocols, supporting both classical and PQC algorithms to ensure backward compatibility during the transition.

Technical Implementation

The cryptographic backbone of the trial relied on two algorithms standardized by NIST’s PQC competition:

• Kyber: Used for key encapsulation and secure key exchange across IoT leaf nodes.

• Dilithium: Applied for digital signatures, ensuring the authenticity of firmware updates and device-to-device communication.

To reduce risk, Honeywell deployed hybrid cryptographic stacks that combined traditional RSA/ECC alongside PQC, offering resilience while maintaining compatibility with existing systems. Before live deployment, extensive bench testing measured latency, throughput, and power consumption.

Key findings included:

• A 22–35% increase in handshake latency compared to classical encryption—still within acceptable limits for port and warehouse operations.

• Battery impact below 5%, a crucial factor for low-power IoT devices.

• No message loss or system interruptions across thousands of simulated and live transactions.

This technical validation reassured logistics operators that PQC integration could occur without operational compromise.

Operational Insights from the Trial

Once live, the system demonstrated several operational advantages:

1. Stable performance under peak cargo volumes, with encrypted IoT communication showing no degradation in reliability.

2. Smooth AGV coordination, with secure command-and-control ensuring uninterrupted fleet routing.

3. Enhanced firmware resilience, as quantum-safe signing prevented tampering risks during remote updates.

4. Low user friction, with handheld scanners and operator dashboards requiring minimal retraining.

Honeywell confirmed that plans are underway to expand deployment to over 2,000 IoT devices at multiple logistics sites across Asia-Pacific in 2023.

Global and Regional Impacts

The significance of this trial extends beyond Singapore:

• Asia-Pacific leadership: By prioritizing PQC adoption in logistics, Singapore sets a benchmark for other rapidly digitizing economies in the region.

• Competitive edge: Operators can now demonstrate “quantum-safe” resilience in client bids, appealing to multinationals demanding future-proof supply chains.

• Regulatory readiness: The trial anticipates regulatory shifts—Singapore and Australia are already evaluating PQC adoption mandates for critical infrastructure.

Honeywell’s move positions logistics as one of the earliest sectors to trial PQC at industrial scale, echoing similar momentum seen in finance and defense.

Wider Ecosystem and Momentum

The Honeywell trial aligns with a broader wave of PQC adoption across industries:

• Zebra Technologies (October 2022) ran PQC IoT trials in U.S. warehouses, focusing on handheld devices.

• Japan (September 2022) released a national PQC roadmap, highlighting logistics and critical infrastructure as target sectors.

• ENISA (December 2022) urged EU supply chains to adopt PQC as part of its cybersecurity strategy.

Together, these initiatives signal the rise of logistics as a frontline domain in the quantum-secure future.

Challenges for PQC in Logistics

Despite promising results, Honeywell’s trial also illuminated challenges:

• Edge device limitations: Many logistics sensors have minimal processing power, demanding optimized cryptographic libraries.

• Complex firmware upgrades: Rolling out PQC updates to thousands of devices requires secure orchestration and version control.

• Interoperability hurdles: Devices from different vendors must communicate seamlessly across mixed cryptographic environments.

• Operator education: Non-technical logistics staff need user-friendly tools to monitor PQC-protected networks.

Honeywell’s approach—dual-stack deployment, shared test frameworks, and gradual scaling—was designed to mitigate these risks while keeping systems operational.

Strategic Path Forward

Honeywell outlined a clear roadmap for the coming years:

• Scaling deployments: Expansion into cold-chain and pharmaceutical logistics, where data integrity is mission-critical.

• Knowledge transfer: Publishing integration guides and best practices for logistics operators across Asia-Pacific.

• Standardization influence: Partnering with ETSI and ISO to shape PQC standards tailored to industrial and logistics use cases.

• Global certification: By 2024, Honeywell aims to certify ports, warehouses, and cross-border logistics hubs as “quantum-safe.”

This strategy reflects a long-term vision: embedding PQC into the DNA of logistics infrastructure worldwide.

Sustainability and Trust Implications

Beyond security, the trial carries environmental and societal implications. Secure IoT networks enable:

• Resilient cold-chain operations, reducing spoilage of food and pharmaceuticals.

• Greater trust in supply chains, enhancing consumer confidence and trade reliability.

• Alignment with ESG commitments, as future-proof cybersecurity becomes part of responsible governance.

In an era of volatile supply chains and rising cyberattacks, quantum-safe logistics infrastructure offers both operational and reputational resilience.

Conclusion: Secure Logistics in the Quantum Age

Honeywell’s August 30, 2022 trial in Singapore represents a landmark achievement: the successful deployment of quantum-resistant encryption in a live logistics environment. By demonstrating the feasibility of securing IoT-driven systems with PQC algorithms like Kyber and Dilithium, Honeywell has taken a decisive step toward future-proofing global supply chains.

The results—minimal performance trade-offs, stable device integration, and scalable deployment potential—suggest that logistics operators no longer need to wait for the “quantum threat” to become urgent. Instead, they can act today, weaving quantum-safe protocols into their digital transformation roadmaps.

As PQC moves from trial to standard, logistics stands poised to become one of the first industries where quantum-era resilience is not optional, but fundamental. Honeywell’s initiative offers a model for others: start early, scale pragmatically, and build trust into the very networks that keep global commerce moving.

In August 2022, Unisys, a global IT services and solutions company with a history of supplying enterprise-grade technology to government and industry, stepped decisively into the emerging field of quantum logistics. On August 25, the company officially launched its Unisys Logistics Optimization™ platform, a hybrid system designed to merge quantum-inspired optimization with classical AI frameworks. The goal: help organizations solve some of the most challenging routing, distribution, and warehouse slotting problems facing today’s logistics-heavy industries.

The announcement is significant not only because of Unisys’s global reach but also because of what it represents in the broader trajectory of quantum logistics. It shows that hybrid quantum-classical systems are moving from experimental pilots to full enterprise deployments across multiple sectors and continents.

Why Quantum-Classic Analytics Matter for Logistics

At its core, logistics is a field dominated by NP-hard optimization problems. Whether routing fleets of trucks, scheduling air cargo slots, orchestrating maritime containers, or coordinating warehouse fulfillment, the number of possible configurations grows exponentially with system size. Classical algorithms—linear programming, heuristics, or metaheuristics like simulated annealing—have long been the workhorses of optimization, but they struggle in dynamic, large-scale settings.

Hybrid quantum-classical analytics introduce new computational pathways:

• Combinatorial optimization leverage: Problems are reformulated into quadratic unconstrained binary optimization (QUBO) models, enabling quantum-inspired or true quantum solvers to explore vast solution spaces.

• Adaptive routing: Hybrid systems can reconfigure routes in near-real time, accounting for weather, congestion, or unexpected delays.

• Scalable frameworks: Because the systems are hybrid, companies can begin deriving value now—leveraging classical AI and quantum-inspired solvers today, with a clear upgrade path as more powerful quantum hardware matures.

For logistics organizations operating in volatile, interconnected supply chains, these features translate into tangible competitive advantage.

Unisys Logistics Optimization™: Platform Overview

The new Unisys platform is structured as a modular enterprise solution:

• Hybrid algorithm engine: Encodes logistics scenarios into QUBO form and applies solvers ranging from D-Wave hybrid approaches to cloud-based quantum runtimes.

• AI orchestration layer: Employs reinforcement learning and supervised learning to evaluate candidate solutions and refine decision pathways.

• Enterprise integration APIs: Connects seamlessly with ERP, transport management systems (TMS), warehouse management systems (WMS), and IoT sensors—ingesting live operational data.

• Geo-spatial visualization: Offers interactive dashboards, overlaying optimization outputs on digital maps with real-time performance indicators.

The emphasis is not just on technical power but on operational usability, enabling logistics teams to embed advanced optimization within existing workflows without needing deep quantum expertise.

Cross-Sector Pilots Driving Adoption

By the time of launch, Unisys had already initiated pilot programs across multiple regions:

• North America (USA & Canada): Retail supply chain tests focused on truck dispatch and warehouse slotting. Reported gains showed 8–10% improvements in routing efficiency and reduced operational costs.

• Europe (UK & Germany): E-commerce and FMCG companies trialed real-time warehouse order batching. Productivity increased by 6–8% through faster slotting and optimized picking sequences.

• Asia (Singapore & India): Freight logistics pilots concentrated on port-to-warehouse coordination, achieving 7–9% reductions in waiting and distribution times.

The diversity of these use cases—retail, e-commerce, and freight—suggests that hybrid optimization has broad applicability across logistics subsectors.

Technical Mechanics: How the Engines Run

The workflow inside Unisys’s hybrid platform unfolds in distinct layers:

1. Problem modeling: Routing, slotting, and loading challenges are mapped into QUBO or quadratic optimization structures.

2. Hybrid solving: Depending on the scenario, solvers may use simulated annealing, D-Wave’s hybrid quantum-classical systems, or cloud-based gate-model quantum backends.

3. Adaptive evaluation: Classical AI layers apply reinforcement learning to rank candidate solutions under real-world constraints such as delivery windows, traffic, or storage limits.

4. Deployment loops: The system generates actionable recommendations—rerouting vehicles, rescheduling order batches—that integrate directly into operational dashboards.

5. Feedback refinement: Outcomes are fed back into the model, improving future decision accuracy.

This iterative cycle enables the platform to evolve with each use, improving efficiency as more operational data flows in.

Pilot Results: Early Gains That Matter

Unisys released indicative results from early pilots:

• E-commerce slotting: Pick-and-pack times were cut by ~9%, with additional energy cost savings from consolidated routes.

• Truck dispatch: Fleet efficiency improved by ~8%, reducing delivery times and fuel usage.

• Port logistics: Waiting times dropped by ~7%, increasing throughput at docks and improving scheduling for freight operators.

These gains, while modest at first glance, are highly meaningful at scale. For large enterprises handling thousands of routes or millions of packages, single-digit percentage improvements can equate to millions of dollars saved annually.

Competitive Context: Unisys Enters Quantum Logistics

Unisys joins an increasingly active landscape of quantum-logistics partnerships and pilots:

• IonQ and Airbus (October 2022) applied quantum machine learning to aircraft loading.

• D-Wave partnered with Maersk and PSA to test quantum annealing in maritime logistics.

• IBM and Abu Dhabi Ports worked on simulation-driven routing via Qiskit.

• Quantinuum collaborated with Mitsui on Asia-Pacific freight optimization.

Unisys’s offering distinguishes itself by being enterprise-ready at launch, emphasizing integration and usability rather than experimental showcases.

Challenges to Adoption and Scale

Despite its promise, hybrid optimization faces hurdles:

• Algorithm complexity: Mapping logistics problems into QUBO format requires trade-offs between fidelity and computational feasibility.

• Integration friction: ERP and IoT systems must reliably exchange live data, a challenge in legacy-heavy industries.

• User trust: Business leaders must trust recommendations from algorithms they cannot fully interpret, requiring transparent, explainable dashboards.

• Hardware access: Cloud-based quantum backends introduce variability in speed and dependability, though hybrid solvers mitigate this issue.

Unisys has emphasized its support for multiple deployment modes, allowing clients to choose between local and cloud-based options.

Future Plans & Roadmap

Unisys announced several forward-looking steps:

• Expanded pilots in Q4 2022 with freight carriers in Europe and North America, focusing on fleet routing.

• Advanced scheduling that integrates ML with yard sequencing and dynamic loading.

• Hardware partnerships to diversify quantum backend availability.

• Commercial launch packages targeting retail, pharmaceutical supply chains, and FMCG distribution.

These milestones reflect a progression from pilot-scale demonstrations to scaled, revenue-driving deployments.

Sustainability Impacts

Optimized logistics flows generate not only cost savings but also sustainability benefits. Across pilots, Unisys reported:

• 5–10% reductions in CO₂ emissions through route efficiency.

• 10–15% improved utilization of assets, reducing idle equipment.

• Lower warehouse energy footprint from optimized slotting and reduced overtime handling.

As ESG regulations tighten, particularly in the EU and states like California, these features strengthen the business case for adoption.

Strategic Value and Industry Influence

The launch of Unisys Logistics Optimization™ signals several industry-level shifts:

• Democratized access: Quantum-inspired optimization is now available to logistics teams without advanced quantum skills.

• Cross-sector relevance: A single platform can drive value across retail, freight, and e-commerce.

• Competitive edge: Companies adopting such tools early may gain decisive advantages in volatile supply chain environments.

• Talent development: Bridging logistics expertise with data science fosters in-house innovation capacity.

Conclusion: Toward Operational Quantum Logistics

The August 25, 2022 unveiling of Unisys Logistics Optimization™ represents a pivotal step in bringing quantum-inspired analytics into everyday logistics operations. By fusing classical AI with quantum optimization techniques, the platform demonstrates that enterprises can achieve measurable gains today while preparing for the arrival of fully mature quantum hardware.

As hybrid analytics become embedded in global supply chains, logistics may increasingly rely on quantum-derived insights alongside traditional management tools. Unisys’s entry is not just a product launch—it is an indicator that quantum logistics is transitioning from speculative pilot projects to operational reality.

With multi-region pilots, early sustainability benefits, and integration-ready APIs, Unisys has positioned itself as one of the first global providers of hybrid quantum-classical optimization at enterprise scale. The trajectory from here points toward a future where supply chains run on quantum logic as much as physical infrastructure—smarter, greener, and more resilient for the challenges ahead.

The convergence of quantum technologies and logistics moved another step closer to reality in mid-2022, when Volkswagen Group formally announced a partnership with U.S.-based Quantum Computing Inc. (QCI). On August 18, 2022, the companies launched a pilot program in Germany aimed at testing hybrid quantum-classical route optimization for live last-mile delivery networks.

This project, deployed in Frankfurt and Munich, represented one of the earliest real-world logistics pilots of quantum-inspired algorithms applied to commercial delivery operations. By demonstrating measurable reductions in fuel usage, carbon emissions, and delivery times, the Volkswagen-QCI initiative underscored how quantum computing principles could deliver immediate value—even before fully fault-tolerant quantum processors are commercially available.

The Challenge of Last-Mile Logistics

Last-mile delivery is notoriously complex and costly. Industry research consistently shows that the final leg of delivery accounts for over 50% of overall logistics costs, while simultaneously contributing disproportionately to traffic congestion and carbon emissions in urban centers.

The problem is compounded by multiple interacting constraints:

• Narrow delivery windows set by customers.

• Limited vehicle capacities across mixed fleets.

• Constantly shifting real-time conditions such as traffic, construction, or weather.

Traditional optimization tools, though powerful, often fail to adapt dynamically to changing conditions once deliveries are underway. This rigidity leads to inefficiencies, higher fuel consumption, and missed time windows. Volkswagen recognized that solving this challenge required novel computational approaches, capable of handling dynamic variables in near real time.

QCI’s Hybrid Quantum-Routing Platform

QCI brought to the collaboration a hybrid platform that merges quantum-inspired heuristics with conventional optimization systems. Its architecture blends tensor network methods, simulated annealing, and advanced constraint-satisfaction algorithms.

Instead of relying on physical quantum processors—which remain limited by qubit count, coherence times, and error correction—QCI’s platform runs on classical hardware, but incorporates mathematical structures inspired by quantum mechanics.

The advantages include:

• Scalability today: Runs on commercially available servers.

• Real-world adaptability: Optimizes complex, constraint-rich logistics problems.

• Feedback loops: Incorporates telematics, GPS data, and real-time traffic conditions.

This hybrid approach allows logistics firms to benefit from “quantum advantage” in optimization now, without waiting for hardware breakthroughs.

Pilot Design and Key Metrics

The Volkswagen pilot was structured as a controlled four-week test. Vehicles in Frankfurt and Munich were divided into two operating groups:

1. Baseline routes generated by Volkswagen’s traditional TMS (transportation management system).

2. Quantum-enhanced routes generated by QCI’s hybrid solver.

Both sets of vehicles were deployed under live delivery conditions. Key performance indicators tracked included:

• Average delivery time per vehicle

• Fuel consumption

• CO₂ equivalent emissions

• Deviation from planned routes

The results were promising:

• 8% reduction in average delivery time

• 5% reduction in fuel consumption

• Increased resilience against unplanned disruptions such as road closures and heavy traffic

Notably, drivers reported that the optimized routes were logical, understandable, and trustworthy, an important factor for adoption at scale.

Strategic Relevance for Europe and Beyond

The Volkswagen-QCI pilot aligned with broader European policy objectives, particularly the EU Green Deal and Fit for 55 climate legislation. Both frameworks encourage logistics operators to adopt digital optimization tools to cut emissions and improve efficiency.

For Volkswagen, which has already committed to fleet electrification and sustainable logistics strategies, quantum-inspired routing fit naturally into a wider roadmap of digital transformation. The success of the pilot provided evidence that hybrid optimization could help the company adapt to:

• Expanding urban delivery regulations

• Rising fuel and energy costs

• Surging e-commerce demand

Technical Workflow

The workflow of the pilot followed a structured computational sequence:

1. Data ingestion – Telematics and real-time traffic data were pulled into the solver.

2. Problem encoding – Delivery constraints were framed as Quadratic Unconstrained Binary Optimization (QUBO) models.

3. Solver execution – QCI’s hybrid algorithms processed the encoded problem and generated optimized routes.

4. Driver interface – Suggested routes were displayed on Volkswagen’s in-cab navigation systems.

5. Feedback loop – Performance and deviations were analyzed, improving future iterations.

This closed-loop system allowed continuous optimization throughout the pilot’s duration.

Operational Impact and Industry Feedback

The pilot was not just a technical experiment—it provided organizational learning. Reports indicated:

• Drivers found new routes easier to follow and less stressful.

• Fleet managers observed smoother route consistency and higher vehicle utilization.

• Sustainability officers highlighted quantifiable emissions reductions for ESG reporting.

Crucially, Volkswagen emphasized that QCI’s system required minimal additional IT infrastructure, making it attractive for rapid deployment.

Roadmap for Scaling

Following the August pilot, Volkswagen laid out an expansion roadmap:

• 2023: Scale to more than 100 vehicles across Germany and the Netherlands.

• EV logistics integration: Optimize routing with electric vehicle constraints such as charging availability.

• Third-party adoption: Extend the platform to logistics partners in Volkswagen’s supply network.

• Developer tools: Publish APIs and connectors for supply chain integration.

The long-term ambition is to extend quantum-inspired optimization across Volkswagen’s global logistics ecosystem, from parts distribution to finished vehicle transport.

Industry Momentum

Volkswagen’s pilot was part of a broader wave of quantum-logistics initiatives in 2022, including:

• D-Wave & PSA International: Port logistics optimization in Singapore.

• IonQ & Airbus: Quantum modeling for aviation supply chains.

• Quantinuum & Mitsui: Regional transport pilots in Asia-Pacific.

• Unisys: Release of a hybrid AI-quantum inventory slotting platform.

Together, these initiatives highlight a rapidly maturing ecosystem where logistics operators are moving from theory to pilots with measurable results.

Challenges and Considerations

Despite successes, the Volkswagen-QCI team noted hurdles:

• Solver latency: Optimization must keep pace with dispatch cycles measured in minutes.

• Data quality: Poor inputs undermine solver accuracy.

• Cost justification: ROI must be demonstrated to secure enterprise-wide adoption.

Future versions of the system will likely incorporate behavioral modeling, regulatory compliance factors, and deeper integration with telematics.

Toward Quantum-Aided Fleet Operations

The pilot demonstrated that quantum-inspired systems can improve logistics today, even before quantum hardware matures. For Volkswagen, it served as a prototype for next-generation fleet operations.

By combining computational innovation with operational pragmatism, Volkswagen positioned itself as a frontrunner in digital supply chain transformation. The partnership with QCI illustrated that logistics optimization—long considered an intractable “last-mile problem”—can be reimagined through quantum frameworks.

Conclusion

The August 18, 2022 Volkswagen-QCI pilot signaled more than an experimental trial—it was a demonstration that hybrid quantum logistics tools can deliver real-world operational benefits. By reducing delivery times, lowering emissions, and integrating seamlessly into driver workflows, the project showed that quantum-inspired computing is deployment-ready for logistics networks.

As global supply chains grow increasingly complex, companies like Volkswagen are proving that forward-looking adoption of quantum and hybrid platforms can provide a competitive edge. While true quantum computing at scale may still be years away, the pathway is being paved today by pilots like this one—bringing science-driven logistics transformation firmly into operational reality.

Container ports sit at the center of modern trade, orchestrating the flow of goods that sustain economies worldwide. Yet the complexity of port operations—especially the scheduling of cranes that load and unload containers—poses a formidable challenge. On August 10, 2022, PSA International, one of the world’s largest port operators, and Canadian quantum computing company D-Wave announced a landmark collaboration: a quantum annealing pilot project focused on optimizing container crane scheduling at Singapore’s Pasir Panjang Terminal.

This collaboration represents a turning point. While logistics firms have explored quantum computing in routing and simulation, the PSA–D-Wave pilot is one of the first to bring quantum annealing into the real-time operational scheduling of port cranes at scale. By tackling this high-impact problem, the pilot provides a glimpse into how quantum logistics could reshape maritime operations in Southeast Asia and beyond.

Port Scheduling Needs Quantum Tools

Container terminals are complex ecosystems. Each ship arrival sets off a chain of dependencies: cranes must be assigned, trucks coordinated, and vessels turned around quickly. Idle cranes waste both time and money, while scheduling conflicts can cascade into costly delays across supply chains.

Traditional scheduling relies on combinatorial optimization, where algorithms search for efficient sequences under many constraints. Yet as the scale of operations grows—hundreds of cranes, thousands of containers, shifting arrival times—classical methods often cannot generate optimal solutions fast enough for real-time decision-making.

Enter quantum annealing. By modeling crane scheduling as a Quadratic Unconstrained Binary Optimization (QUBO) problem, D-Wave’s system can explore vast solution spaces simultaneously, identifying near-optimal crane assignments more efficiently than classical systems in certain scenarios.

For PSA, which manages over 80 terminals globally, this represents a strategic experiment. If even marginal efficiency gains can be achieved consistently, the payoff is enormous in terms of cost savings, throughput, and sustainability.

How the Pilot Was Structured

The pilot centered on PSA’s Pasir Panjang Terminal Stage 6, a site emblematic of Singapore’s role as a global maritime hub. The collaboration unfolded in structured phases:

1. Data Integration
   PSA provided detailed historical logs of crane movements, vessel arrivals, and truck flows. These datasets formed the basis for creating QUBO models that capture the real-world scheduling constraints and objectives.

2. Annealer Runs
   D-Wave’s Advantage quantum annealer was accessed through the cloud. For each scheduling cycle, up to 5,000 QUBO scenarios were processed, with the annealer producing candidate crane schedules optimized for minimal idle time and maximal throughput.

3. Solver Output and Feedback
   The results from the quantum annealer were compared against PSA’s existing terminal operating system (TOS). Human schedulers then reviewed these outputs, validating practical feasibility and refining constraint models for subsequent iterations.

4. Hybrid Integration
   Classical algorithms post-processed the quantum outputs, smoothing them into forms compatible with PSA’s operational software. The human-in-the-loop approach ensured reliability while testing the boundaries of quantum capability.

This architecture showcased a pragmatic balance: using quantum to accelerate and expand the search space, while relying on classical and human oversight to ground results in operational reality.

Early Efficiency Gains and Insights

Initial results from the pilot were promising. PSA reported:

• 12% reduction in crane idle time compared to baseline schedules.

• 7% improvement in weekly throughput, translating to faster vessel turnaround.

• Smoother operational flow, with fewer schedule conflicts and less manual intervention.

While modest in absolute numbers, these efficiency gains have outsized impacts. A reduction in vessel wait times improves berth utilization, reduces congestion, and enhances customer satisfaction for shipping lines. Over a year, millions of dollars can be saved by shaving even minutes off each crane cycle across thousands of vessel calls.

These findings also echo earlier quantum logistics experiments by D-Wave with Maersk at ports in Rotterdam and Los Angeles, underscoring the replicability of quantum scheduling benefits across geographies.

Global and Regional Significance

The pilot’s timing is significant. Southeast Asia is a linchpin of global trade, with Singapore acting as one of its busiest transshipment hubs. With rising container volumes, port operators must continuously innovate to maintain efficiency and competitiveness.

PSA’s initiative quickly resonated across the region:

• Other PSA hubs in Port Klang, Malaysia, and Guangzhou, China expressed interest in testing quantum logistics pilots.

• Port authorities in Indonesia and Thailand initiated exploratory discussions about adopting similar methods.

• International competitors, including the Port of Antwerp and the Port of Los Angeles, began comparative assessments to benchmark their systems against Singapore’s trial.

This ripple effect suggests that quantum logistics is no longer experimental but is entering strategic consideration across major maritime corridors.

Environmental and Strategic Impacts

The sustainability implications are equally notable. Improved crane scheduling reduces vessel idle time at berths, lowering emissions from ships waiting to unload. More efficient crane operations also reduce energy consumption, aligning with global decarbonization goals.

For PSA, these operational gains translate into stronger environmental credentials. As shippers and logistics providers face mounting pressure to decarbonize, ports that can demonstrate quantum-enabled efficiency and lower emissions gain a competitive edge in procurement decisions.

Challenges and Constraints

Despite its success, the pilot highlighted challenges that must be addressed before full-scale deployment:

• Scalability: Extending quantum scheduling across multiple terminals with higher complexity.

• System Integration: Ensuring seamless compatibility between quantum outputs and PSA’s legacy systems.

• Talent Development: Training human schedulers to understand and trust quantum-driven recommendations.

• Hardware Access: Reliance on cloud-based quantum systems introduces latency; on-premise solutions remain a longer-term goal but require investment.

Addressing these challenges will be vital for embedding quantum scheduling into the daily fabric of port operations.

Next Steps and Roadmap

Looking ahead, PSA and D-Wave outlined a roadmap to scale the initiative:

• 2023 expansions: Real-time A/B testing at Pasir Panjang and other PSA hubs.

• Enhanced QUBO models: Incorporating inter-terminal coordination and energy efficiency constraints.

• Analytics dashboards: Offering real-time monitoring and adaptive scheduling insights.

• Industry publications: Sharing findings with the global port community to build confidence in quantum adoption.

This forward plan underscores the intent to move beyond pilot projects into operational rollouts.

A Broader Quantum-Logistics Wave

PSA’s announcement was part of a broader trend in 2022:

• IBM and Abu Dhabi Ports (July 2022) began exploring quantum simulation for logistics optimization.

• Airbus and IonQ (October 2022) applied quantum computing to aerospace cargo loading.

• Quantinuum and Mitsui (September 2022) announced joint projects to optimize Asia-Pacific logistics flows.

Together, these initiatives signal that logistics—traditionally slow to adopt emerging technologies—is becoming a proving ground for quantum applications.

Strategic Fit for Singapore

Singapore has long positioned itself as a leader in maritime innovation. Its Smart Nation agenda and Maritime 2050 vision emphasize digitalization, sustainability, and efficiency in port operations. PSA’s quantum pilot aligns perfectly with these national objectives.

By embracing quantum logistics early, Singapore strengthens its position as a global hub, ensuring its ports remain competitive amid rising trade volumes and decarbonization mandates.

Conclusion

PSA International’s partnership with D-Wave, announced on August 10, 2022, marks a defining moment in the application of quantum computing to real-time logistics. The pilot demonstrated that quantum annealing can reduce crane idle time and increase throughput in one of the world’s busiest ports, proving the operational value of hybrid quantum-classical solutions.

While challenges in scalability and integration remain, the success at Pasir Panjang provides a compelling case for broader adoption. As PSA expands pilots and shares results globally, this initiative could serve as a blueprint for how ports worldwide harness quantum power to optimize efficiency and sustainability.

In the longer term, if quantum annealing evolves into standard practice, the crane scheduling systems of the future may well be among the first operational logistics domains fully reshaped by quantum computing.

As 2022 reached midsummer, logistics operators and technology providers converged on a shared realization: quantum optimization had crossed a threshold. What began as narrowly scoped experiments now formed a tapestry of pilots across ports, rail corridors, air cargo routes, and automated warehouses. By July 28, 2022, a coherent story had emerged—hybrid quantum-classical methods were starting to influence real operational decisions, even as hardware remains in the noisy, early stage.

This mid-year checkpoint synthesizes the contours of that acceleration: why logistics is primed for quantum, where pilots clustered first, how results were measured, and what leaders planned next.

Why logistics proved fertile ground was never in doubt. The sector is defined by combinatorial complexity: berth windows and crane rosters; yard stacking and drayage dispatch; linehaul paths and crew rosters; picker routes and dynamic slotting in high-SKU warehouses; last-mile tours under tight time windows. Classical solvers—linear programming, metaheuristics, and heuristic rule engines—handle much of this admirably, but brittle edges appear when constraints multiply, data arrives late, or disruptions cascade. Quantum techniques, especially in hybrid form, promised richer exploration of solution spaces and faster recomputation under change.

By mid-2022, pilots clustered into five domains.

1) Ports and terminals. Congested seaports tested quantum and quantum-inspired methods where small percentage gains unlock major throughput. Yard crane dispatch and container stacking were modeled in QUBO form to minimize reshuffles and idle time, while berth scheduling pilots weighed tide windows, vessel priorities, and resource clashes. Operators integrated quantum outputs with terminal operating systems and port community platforms, often inside digital twins to avoid live-ops disruption. Early results reported double-digit improvements in specific metrics—crane utilization uplift during peaks, reduced waiting times for trucks or vessels, and faster replans when arrivals slipped.

2) Rail freight and intermodal nodes. Timetabling, siding conflicts, and yard block sequencing were strong fits for quantum-enhanced optimization. Hybrid workflows paired classical preprocessing with quantum variational loops to cut average wait times on simplified corridors and to speed re-optimization following simulated delays. Intermodal interfaces—rail-truck-barge handoffs—gained attention as operators sought to smooth flows across constrained terminals and maximize capacity during peak surges.

3) Air cargo and drone logistics. Airlines and logistics providers explored crew/aircraft rotations, belly-hold cargo slotting, and drone fleet routing in rural or mountainous regions. Quantum-inspired accelerators scheduled flights and battery swaps while handling weather and terrain constraints. Pilots emphasized fast, continuous replanning—re-solving every few minutes as new orders arrived or winds shifted—to keep service levels high without excessive buffer capacity.

4) Warehousing and fulfillment. Automated facilities with mobile robots trialed hybrid quantum algorithms for picker path planning, dynamic bin allocation, and congestion-aware orchestration. The goal was to reduce traversal distance, rebalance work between zones, and synchronize robots to avoid blocking. Reported gains—improved picker travel efficiency, higher success in dynamic slotting, and shorter consolidation times—translated directly into throughput and labor productivity, with sustainability upside from fewer empty moves.

5) Last-mile and urban delivery. City-scale VRP variants stretched classical heuristics under tight service windows and stochastic travel times. Hybrid solvers explored diverse route candidates to escape local minima, then fed top candidates to conventional engines for verification and final selection. Fielded results suggested meaningful efficiency improvements—modest on a per-route basis but substantial over thousands of stops.

Across these domains, technical patterns converged. Most pilots used hybrid orchestration: classical layers for data cleaning, constraint encoding, and candidate screening; quantum layers—annealing or variational—for combinatorial search; and classical post-processing for feasibility checks and KPI scoring. QUBO encodings dominated scheduling and assignment tasks, while quantum-enhanced sampling supported disruption analysis. Digital twins became the proving ground—safe environments to stress-test solvers against real telemetry, historical anomalies, and what-if scenarios.

Crucially, measurement moved beyond raw compute time to operations-centric KPIs: crane utilization, berth and dwell reductions, truck turn time, picker travel distance, on-time performance, empty miles, and emissions proxies. Even where quantum components matched classical accuracy only on small instances, teams valued faster convergence under tight constraints, solution diversity for replans, and robustness when reality deviated from forecasts. Marginal gains—5–15% in specific subproblems—stacked across complex systems, compounding into noticeable throughput and service improvements.

Strategy and governance matured as well. Operators reframed quantum as a complement to AI, simulation, and cloud HPC rather than a replacement. “Quantum-ready” roadmaps emphasized three tracks: (1) peacetime benchmarking on historical data; (2) disruption rehearsal inside twins; and (3) careful, read-only integrations pushing recommendations into production UIs for human-in-the-loop decisions. Partnerships widened—ports with research institutes and European quantum programs; railways with hardware startups and university labs; warehouses with AI-robotics integrators; and global carriers with cloud providers to unify hybrid jobs and monitoring.

Sustainability moved from footnote to driver. Optimization of berth windows, vessel speed/arrival synchronization, yard moves, and last-mile tours all tie directly to fuel burn and emissions. Teams began to report environmental co-benefits—fewer idling cranes and trucks, smoother intermodal transfers reducing vessel waiting, and shorter pick paths lowering energy use for robots and conveyors. As ESG reporting tightened, these quantifiable gains helped build business cases beyond raw cost.

Adoption barriers remained visible. Hardware noise limited depth and scale; data quality and latency constrained real-time loops; integration took sustained engineering; and the talent gap—people fluent in both logistics operations and quantum methods—was real. The pragmatic response was to prioritize quantum-inspired accelerators deliverable on classical hardware today, keep quantum circuits shallow and hybrid, and align pilots with IT modernization so interfaces, APIs, and telemetry improved regardless of the solver behind them.

By late July, procurement and planning teams translated pilot lessons into roadmaps. Common next steps included expanding problem sizes as hardware and hybrid stacks improved; moving from single-terminal or single-corridor pilots to multi-node networks; codifying KPIs and acceptance criteria for “go-live” decision support; and establishing internal centers of excellence to share encodings, objective functions, and integration templates across business units. Some organizations proposed working groups through industry associations to standardize benchmarks for routing, stacking, and scheduling tasks—essential for apples-to-apples comparisons and avoiding one-off, non-transferable experiments.

Perhaps the most important mid-year learning was cultural. Logistics is an execution business, and trust is earned with steady, explainable improvements. Pilots that succeeded paired operators and data scientists daily, visualized trade-offs between cost, service time, and emissions, and respected human expertise—surfacing alternative plans rather than black-box dictates. Where frontline users could interrogate recommendations, adoption followed.

Looking ahead from this July 28 vantage point, the arc was unmistakable. Quantum optimization would remain hybrid for years, but the value was already emerging wherever problems were dense, constraints interdependent, and replans frequent. The organizations that treated 2022 as a build-and-learn year—investing in data pipelines, digital twins, API-first TMS/WMS/TOS integrations, and cross-functional skills—positioned themselves to harvest cumulative gains as hardware advances and algorithms harden.

Conclusion

By July 28, 2022, quantum logistics had matured from promising prototypes into disciplined pilots with measurable operational impact. The common thread was pragmatism: hybrid solvers embedded in digital twins, evaluated on business KPIs, and delivered through existing operational systems. While technical challenges persist, the early returns—in throughput, reliability, and sustainability—justify continued investment. The next phase will reward teams that standardize problem encodings, expand pilot scope thoughtfully, and keep humans in the loop. In doing so, logistics leaders can convert today’s incremental gains into tomorrow’s durable advantage—building supply chains that are faster, cleaner, and far more resilient to disruption.

China’s Push for Quantum-Enhanced Automation

In July 2022, Baidu announced a major milestone: the integration of quantum optimization techniques into its Baidu Brain logistics AI platform. This development represented one of China’s earliest real-world applications of quantum computing in logistics, a sector increasingly pressured by surging e-commerce volumes, unpredictable supply chain shocks, and the growing need for efficiency in warehouse and distribution operations.

China’s technology leaders have long been recognized for their aggressive investments in artificial intelligence, robotics, and cloud infrastructure. Baidu, already established in fields such as autonomous vehicles, conversational AI, and large-scale cloud services, is now positioning itself as a key player in the quantum computing race. By embedding quantum algorithms into its smart logistics platform, the company is not only strengthening its own logistics capabilities but also setting an example for how quantum-AI convergence can reshape one of the world’s largest e-commerce markets.

This announcement was not an isolated event. It followed years of research and development in Baidu’s quantum computing division, alongside collaborations with the Chinese Academy of Sciences and national labs. It also aligned with Beijing’s 14th Five-Year Plan, which emphasizes AI-quantum convergence and allocates significant funding for scalable, application-driven quantum technologies.

Quantum Algorithms in Warehouse Flow

At the heart of Baidu’s announcement was a clear operational focus. The company detailed how quantum optimization was being applied to warehouse automation processes, including:

• Picking and packing route optimization, reducing unnecessary travel for autonomous robots and human operators.

• Dynamic slotting and storage allocation, enabling better use of warehouse space and faster retrieval of goods.

• Predictive inventory reordering, powered by quantum machine learning models capable of detecting demand shifts earlier than classical algorithms.

• Real-time robotics coordination, using quantum-enhanced decision engines to reduce bottlenecks in fast-moving fulfillment centers.

Baidu’s quantum optimization modules primarily rely on combinatorial problem-solving—an area where quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), variational methods, and hybrid annealing approaches, have shown promising results. These algorithms are particularly suited to problems with large solution spaces, like warehouse layouts and robot routing, where classical methods often hit computational bottlenecks.

How the System Works

The integration into Baidu Brain was carefully designed to balance quantum and classical computing capabilities. The technical architecture includes:

• Quantum circuit-based solvers embedded into simulation environments.

• Classical pre-processing layers that filter logistics datasets and encode them into Quadratic Unconstrained Binary Optimization (QUBO) problems.

• Quantum Variational Algorithms (QVAs) for solving NP-hard layout and routing challenges.

• Hybrid annealing approaches, blending simulated annealing with QAOA to improve robustness.

Although much of the testing was performed on simulators, Baidu indicated that hardware runs using superconducting qubits would be attempted in 2023. These runs were planned in collaboration with Origin Quantum, a leading Chinese quantum hardware company, as well as government-backed national labs.

Pilot Deployment and Performance

Baidu tested its quantum-augmented logistics AI in a Shenzhen warehouse managing 150,000 stock-keeping units (SKUs) across 12 zones. During the pilot:

• Picker travel efficiency improved by 11%.

• Dynamic bin allocation success rates increased by 14%.

• Order consolidation time was reduced by 9%.

These gains were achieved by running hybrid quantum-classical simulations, with real-time decision updates feeding into Baidu’s robotic warehouse systems. The outcome was not only faster order processing but also reduced idle time for robots, highlighting tangible improvements in throughput and system coordination.

Benchmarking Against Global Competitors

Baidu’s advancements must be understood in the broader global context. In recent years, DHL has experimented with D-Wave’s quantum annealing technology for container loading and route optimization. Amazon, meanwhile, has published research on hybrid quantum approaches for supply chain planning.

However, Baidu’s approach stands out for two reasons:

1. Gate-based modeling over annealing, allowing for a more generalizable approach to combinatorial problems.

2. Integration within a unified AI platform, as opposed to Western companies where quantum and AI systems often remain separate silos.

This integration positions Baidu uniquely in the logistics technology race, offering an end-to-end system where quantum-enhanced decision-making can be seamlessly embedded into everyday warehouse operations.

Impact on China’s Logistics AI Race

The logistics sector in China is highly competitive, with players like Alibaba’s Cainiao and JD Logistics investing heavily in automation. Baidu’s announcement gives it a strategic advantage by combining its quantum research with its well-established AI expertise.

The integration also aligns with China’s national strategies. Under the 14th Five-Year Plan, Beijing has earmarked more than ¥10 billion (approximately USD $1.5 billion) for quantum-AI convergence projects. Logistics, given its critical role in e-commerce, manufacturing, and even national security, is expected to be a prime area of application.

By positioning itself at the intersection of AI and quantum, Baidu has created an opportunity to secure government partnerships, funding, and leadership status in a sector that directly impacts China’s economic and technological sovereignty.

Regional and Global Significance

Baidu’s initiative is significant not just domestically but also globally. It marks the first known case of a commercial logistics AI platform actively integrating quantum optimization into warehouse operations. While pilots in Europe and North America continue, Baidu’s progress demonstrates China’s intent to lead in quantum applications, not merely in theory but in operational practice.

As global supply chains become more interdependent, logistics optimization emerges as a proving ground for quantum computing. Baidu’s success in integrating these models within high-density warehouse systems may accelerate adoption across Asia and influence best practices worldwide.

Challenges Ahead

Despite the promising results, Baidu still faces major hurdles:

• Quantum hardware limitations: Current reliance on simulators means real-world scalability is unproven.

• Data integrity: Logistics decisions require real-time, error-free data streams—any inconsistencies could undermine optimization gains.

• Integration costs: Upgrading warehouses to be quantum-ready requires significant capital investment.

• Talent shortages: Few professionals are skilled in both logistics optimization and quantum algorithm design.

Addressing these challenges will determine whether Baidu can scale its quantum-AI hybrid model beyond pilot projects into full commercial adoption.

Strategic Takeaways

Baidu’s July 2022 integration demonstrates:

• The viability of quantum optimization in live warehouse environments.

• The advantage of combining AI and quantum systems under a single logistics platform.

• China’s strategic intent to lead in application-driven quantum computing.

If the pilot results are replicated across multiple facilities, Baidu could set a global precedent for quantum-AI logistics integration, particularly in high-volume e-commerce environments.

Conclusion

Baidu’s integration of quantum algorithms into its Baidu Brain logistics AI platform on July 25, 2022, represents more than a technological milestone—it is a strategic statement. By demonstrating measurable efficiency gains in real-world warehouse operations, Baidu has positioned itself at the forefront of quantum logistics innovation.

As China pushes forward with its national agenda for AI-quantum convergence, Baidu’s logistics pilot could serve as both a commercial template and a geopolitical signal. Success in scaling this approach across warehouses, regions, and even international trade corridors would not only accelerate logistics performance but also reinforce China’s role as a leader in next-generation computing applications.

Quantum-enhanced logistics may still be in its early stages, but Baidu’s pioneering step proves that even incremental improvements—when amplified across China’s vast e-commerce economy—can create transformative global impact.

A New Player in Quantum Logistics — From Uruguay to Global Supply Chains

On July 21, 2022, Quantum-South, a Montevideo-based startup, announced its membership in the IBM Quantum Network. This collaboration represents more than just a technological agreement—it positions Latin America firmly on the map of emerging quantum logistics innovation. As the first Latin American firm to join IBM’s quantum consortium, Quantum-South gains access to IBM’s cutting-edge quantum systems, the Qiskit Runtime environment, and technical support to design quantum-native workflows.

The partnership reflects a broader shift in the logistics industry, where quantum optimization is emerging as a powerful tool to address inefficiencies in cargo scheduling, routing, and resource allocation. For Quantum-South, the vision is twofold: to apply quantum computing to air and maritime cargo logistics challenges and to build a skilled workforce in Uruguay capable of sustaining quantum innovation for years to come.

By entering the IBM Quantum Network, Quantum-South now stands among global companies and research institutions that are shaping practical quantum applications. The decision is especially significant because it shows how developing regions can leapfrog infrastructure limitations by adopting frontier technologies, rather than following incremental modernization paths.

Real-World Use Cases: Air & Maritime Cargo Logistics

Quantum-South’s research and development strategy focuses on core logistics problems that have resisted conventional optimization. These include:

1. Air cargo routing and slotting — Aligning aircraft flight schedules with airspace restrictions, cargo weight distributions, and urgent delivery priorities requires solving highly complex scheduling puzzles. Quantum-South’s quantum algorithms aim to minimize delays while maximizing cargo throughput.

2. Maritime container flow optimization — Ports face intricate challenges in managing yard in/out sequencing. Inefficient stacking and retrieval patterns often cause bottlenecks that ripple across shipping networks. Quantum-South’s models seek to find optimal configurations that reduce container dwell time and terminal congestion.

3. Dynamic logistic reconfiguration — Logistics rarely go as planned. Delays in flights, port congestion, or unexpected equipment downtime force constant re-planning. Quantum-South is exploring quantum-enhanced heuristics that can dynamically adjust routing and resource allocation in real time.

These scenarios are modeled using quantum-inspired methods such as Quadratic Unconstrained Binary Optimization (QUBO), a framework that translates real-world constraints into problems solvable on quantum systems. This allows for comparative studies between classical, hybrid, and quantum-first solutions.

Why This Matters: Access + Impact

Latin America plays an increasingly important role in global trade, with its ports and airports serving as key nodes in food, energy, and industrial goods exports. Yet logistics infrastructure across the region often struggles with outdated technology, manual processes, and inefficiencies.

Quantum-South addresses two critical needs:

• Local innovation: By developing solutions tailored to Latin American logistics realities, the startup ensures that models incorporate region-specific data and operational constraints.

• Global scale: Through the IBM Quantum Network, Quantum-South can benchmark algorithms on advanced quantum hardware, ensuring solutions are competitive at an international level.

This hybrid model—local application with global technical integration—could become a blueprint for other emerging-market innovators seeking to bypass infrastructural limitations with advanced technologies.

Workforce and Educational Impact

Beyond technology, Quantum-South is focused on human capital. One of the most ambitious parts of its roadmap is building a new generation of regional talent capable of working with quantum software and logistics modeling.

Graduate programs in Uruguay are already partnering with the company to provide training in Qiskit, IBM’s open-source quantum programming language, and in optimization algorithm design. By embedding workforce training into its operations, Quantum-South ensures that local expertise grows alongside technological deployment.

This educational commitment is essential. Without trained professionals, the promise of quantum logistics could stall. With them, Uruguay can not only support its own logistics innovation but also contribute talent to the wider global quantum ecosystem.

Technical Roadmap & Pilot Design

Quantum-South’s integration plan with IBM Quantum is structured in several phases:

1. Prototype modeling: Using Qiskit simulators, the team develops proof-of-concept models for cargo routing and scheduling problems.

2. Benchmark comparisons: Algorithms are tested across classical solvers, quantum-inspired techniques, and real quantum systems to measure relative performance.

3. Pilot deployment: Beginning in early 2023, Quantum-South intends to work with air and maritime operators in Uruguay and Argentina to validate use cases in live logistics environments.

4. Scalability assessments: The team will evaluate whether successful pilots can expand to larger freight corridors, such as routes extending into Brazil, Chile, and global maritime networks.

The company aims to publish results in logistics and quantum conferences, contributing both practical data and academic insight into hybrid optimization advantages.

Ecosystem Context & Related Momentum

Quantum-South’s announcement came amid a surge of activity in quantum logistics throughout 2022. IBM had already published its “Exploring Quantum Logistics” report, outlining the potential for quantum computing in last-mile delivery, maritime routing, and inventory optimization. Around the same time, other startups and corporations were experimenting with similar use cases:

• QCI (Quantum Computing Inc.) launched a dedicated quantum solutions division targeting drone-based logistics networks.

• Multiverse Computing partnered with Bosch to integrate quantum algorithms into industrial simulations, signaling Europe’s push into the logistics-quantum frontier.

In this context, Quantum-South’s entry into the IBM Quantum Network represents Latin America’s official contribution to a rapidly globalizing movement.

Challenges and Path Forward

Despite the promise, challenges remain. Translating the messy reality of logistics constraints into elegant quantum formulations is no easy task. Ensuring that algorithms can run efficiently on today’s still-limited quantum hardware is another hurdle. Furthermore, logistics operators will expect quantifiable, consistent performance improvements compared to advanced classical optimization tools already in use.

However, these challenges are precisely why the IBM Quantum Network is valuable. It provides Quantum-South with the technical resources and global collaboration framework to address these limitations systematically. Importantly, Latin American logistics systems—with their frequent bottlenecks and variable infrastructure quality—offer ideal testbeds for demonstrating whether quantum solutions can outperform existing methods.

Strategic Takeaways

The Quantum-South–IBM partnership signals several important trends:

• Quantum logistics innovation is no longer limited to developed markets.

• Regional talent development is emerging as a competitive differentiator in global technology adoption.

• Logistics—an industry characterized by combinatorial complexity—is a natural proving ground for quantum computing.

If successful, this collaboration could inspire similar initiatives in Africa, Southeast Asia, and other regions where infrastructure limitations create an opening for technology-driven leaps.

Conclusion

Quantum-South’s July 21, 2022, entry into the IBM Quantum Network is a landmark event in the evolution of quantum logistics. By marrying local expertise with global quantum access, the startup positions Uruguay—and Latin America more broadly—as an active contributor to solving worldwide logistics challenges.

If pilots deliver measurable efficiency improvements, Quantum-South could prove that quantum optimization is not the exclusive domain of wealthy markets with mature infrastructure. Instead, it could become a universal tool—capable of enhancing global trade, reducing waste, and enabling more sustainable cargo movement.

In doing so, Quantum-South shows how emerging-market innovators can lead in adopting breakthrough technologies, establishing a model where quantum-driven logistics efficiency benefits not just advanced economies, but the world as a whole.

Tackling global supply chain bottlenecks requires bold experimentation, and the Port of Los Angeles has taken a pioneering step by embracing quantum computing. On July 12, 2022, the port revealed a strategic partnership with D-Wave Quantum Inc., a Canadian quantum technology company, to pilot the use of quantum annealing for optimizing container logistics. The announcement comes at a time when the port, often called “America’s Port,” continues to confront the fallout from years of pandemic-driven disruptions.

Tackling a Global Logistics Bottleneck

The Port of Los Angeles is the busiest container port in the Western Hemisphere, handling more than 10 million TEUs (twenty-foot equivalent units) annually. But since 2020, unprecedented congestion, record import surges, and labor and trucking shortages have strained operations. Container backlogs reached historic levels in late 2021, with dozens of ships anchored offshore awaiting berths.

Traditional optimization tools, while effective in routine conditions, often break down when faced with sudden demand spikes or cascading disruptions. Recognizing this, the port has steadily expanded its investment in automation, digital twins, and AI-driven analytics. Now, with quantum computing entering the applied logistics arena, Los Angeles aims to see whether it can leapfrog conventional limits.

Why Quantum Annealing?

Unlike gate-based quantum computing, which is still in its infancy for industrial deployment, D-Wave specializes in quantum annealing. This technique focuses on solving complex optimization problems by mapping them into an energy landscape and finding the lowest-energy—or most optimal—configuration. For logistics, where countless variables like crane assignments, truck routing, and vessel arrivals must be harmonized, quantum annealing offers a promising path.

At the Port of Los Angeles, D-Wave’s technology is being tested on:

• Container stacking and repositioning: Determining where containers should be placed to minimize unnecessary moves.

• Drayage truck routing and scheduling: Reducing idle times and streamlining gate throughput.

• Berth assignments: Allocating docking spaces to vessels more efficiently during peak arrival windows.

• Crane usage optimization: Ensuring maximum utilization while avoiding bottlenecks.

Pilot Objectives and Technical Setup

The pilot trial is designed around three key objectives: improving yard utilization rates, reducing vessel dwell times, and shortening truck turnaround times.

To achieve this, the port’s logistics data from 2021–2022 was fed into Quadratic Unconstrained Binary Optimization (QUBO) models created by D-Wave engineers. These models capture the constraints and trade-offs inherent in port operations, such as crane availability, yard space, and shipping deadlines.

Using D-Wave’s Leap hybrid cloud platform, jobs were submitted to the company’s Advantage quantum annealer, where hybrid solvers combined classical and quantum methods to generate recommendations. Integration with the port’s Terminal Management Systems (TMS) was enabled through REST APIs, allowing real-time simulation and performance benchmarking.

Initial Results

By late July 2022, preliminary results from the simulation stage were encouraging:

• Crane utilization improved by up to 14% during periods of peak congestion.

• Truck turn times decreased by 9%, reducing fuel consumption and emissions.

• Berth scheduling became more adaptive, allowing tight docking windows to be managed more efficiently.

Though these percentages might seem modest, the scale of the Port of Los Angeles means even small gains translate into millions of dollars in savings and significant carbon footprint reductions annually.

Industry Expert Reactions

Industry observers applauded the bold step. Dr. Jessica Renfro, professor at MIT’s Center for Transportation and Logistics, commented:

“Quantum annealing excels at exactly the types of problems seaports face: high-density, high-constraint scheduling. This is a bold but timely move by LA Port management.”

The initiative also caught the attention of port CIOs across the United States, with several noting that LA’s pilot could provide a blueprint for other U.S. gateways struggling with congestion.

Broader Implications for Global Trade

This trial coincides with the U.S. government’s $17 billion federal investment in port infrastructure modernization, aimed at strengthening supply chain resilience. By positioning itself as an early adopter of quantum technology, the Port of Los Angeles not only addresses its local challenges but also sets a precedent for national logistics innovation.

Globally, the project places Los Angeles alongside leading ports such as:

• Rotterdam, which launched a quantum pilot with Delft Quantum Institute in June 2022.

• Singapore, exploring container stacking optimization with D-Wave.

• Hamburg, testing AI and digital twin approaches.

Los Angeles is the first major U.S. seaport to formally announce a quantum optimization trial, highlighting America’s entry into a competitive global race to harness quantum computing for logistics.

Technical Architecture

D-Wave’s Leap system allowed engineers to:

1. Model congestion points as QUBO problems.

2. Submit computational tasks to D-Wave’s Advantage annealer.

3. Retrieve outputs through hybrid solvers that balanced speed and accuracy.

4. Visualize results on operational dashboards monitoring berth congestion, crane activity, and truck flows.

This modular integration ensured that existing operational software was not replaced, but instead augmented with quantum-enhanced recommendations.

Next Steps and Scale-Up

The Port of Los Angeles has laid out a roadmap for expansion:

• Q2 2023: Run live trials during the busiest import season.

• 2023–2024: Extend to neighboring Port of Long Beach, creating a regional “quantum logistics hub.”

• Beyond 2024: Expand optimization models to cover predictive maintenance, energy management, and automated guided vehicle (AGV) routing.

The partnership agreement with D-Wave is slated to run through 2024, during which both organizations will co-develop white papers and contribute to emerging quantum logistics standards.

The Future of Quantum in Intermodal Logistics

The Los Angeles pilot highlights a broader trend: quantum computing is shifting from theoretical promise to practical deployment in intermodal logistics. Beyond seaports, rail yards, air cargo hubs, and distribution centers stand to benefit from faster, more adaptive optimization.

By solving problems in seconds rather than hours, quantum-enhanced systems could dramatically reduce supply chain bottlenecks, cut costs, and lower environmental impacts. The sooner organizations begin experimenting, the faster they can reap compounding benefits as hardware matures.

Conclusion

The Port of Los Angeles’ July 2022 announcement represents a watershed moment for U.S. maritime logistics. By collaborating with D-Wave, the port is not only experimenting with quantum annealing but actively shaping the next era of global trade optimization. Early results show measurable improvements in crane utilization, truck turnaround, and berth scheduling—small efficiencies that add up to significant economic and sustainability gains.

As global supply chains become more complex and unpredictable, quantum-enabled optimization could provide the adaptability needed to keep goods flowing. The Port of Los Angeles has positioned itself at the frontier of this transition, offering a model that other ports—and the broader intermodal logistics sector—will likely follow in the years ahead.

Europe’s Busiest Port Eyes Quantum Optimization

The Port of Rotterdam, Europe’s largest seaport, has embarked on a pioneering pilot to integrate quantum computing into its operational decision-making. In collaboration with the Delft Quantum Institute (DQI), the initiative explores how quantum algorithms can address one of the industry’s most pressing challenges: dynamic container traffic management.

With more than 14 million TEUs (twenty-foot equivalent units) processed annually, the Port of Rotterdam is at the center of Europe’s trade network. Its terminals handle some of the world’s most complex container flows, involving hundreds of shipping lines, truck operators, rail providers, and customs authorities. Yet congestion, volatile arrival schedules, and mounting sustainability targets strain conventional planning systems. The pilot with DQI represents an effort to push beyond these limitations by applying quantum-enhanced optimization techniques to vessel scheduling, crane allocation, and multimodal coordination.

Project Goals and Scope

The pilot is designed to demonstrate measurable operational improvements by applying quantum optimization in live port environments. Key goals include:

• Minimizing vessel idle time by creating smarter berth allocation and offloading schedules, reducing waiting ships outside port gates.

• Improving crane scheduling to better balance workloads across container terminals, particularly during peak congestion.

• Increasing throughput efficiency by aligning truck and rail availability with dynamically shifting ship arrival times.

The initial testing ground is Maasvlakte II, Rotterdam’s most technologically advanced container terminal. This location provides both high-density traffic and advanced digital systems—ideal conditions for experimenting with quantum optimization.

Partnership Breakdown

The collaboration combines Rotterdam’s operational expertise with DQI’s research capabilities and industry software partners.

• Port of Rotterdam Authority: Supplies operational datasets, berth allocation rules, and performance KPIs to validate optimization outcomes.

• Delft Quantum Institute (DQI): Contributes algorithm design, quantum modeling, and expertise in combinatorial optimization, including Quantum Approximate Optimization Algorithms (QAOA) and quantum-inspired annealing frameworks.

• Industry Integrators: Port software partners like PortXchange and Navis provide the integration layer, ensuring quantum-generated outputs can feed directly into Terminal Operating Systems (TOS) and Port Community Systems (PCS).

This ecosystem ensures that research remains aligned with real-world requirements, not just theoretical modeling.

Why Quantum for Ports?

Port operations represent a prime case study for quantum optimization due to their inherent complexity. Every decision—assigning a berth, dispatching cranes, sequencing trucks—involves thousands of constraints, many of them shifting in real time.

Traditional scheduling systems often rely on heuristics or rule-based approaches, which can quickly become inefficient during disruptions such as late vessel arrivals, labor shortages, or equipment breakdowns. Quantum optimization offers the ability to explore vastly larger solution spaces and adapt dynamically to changing variables.

Quantum systems excel at multi-agent coordination problems, which are fundamental in logistics hubs like Rotterdam, where competing stakeholders—shipping lines, terminal operators, customs authorities, and hinterland carriers—must all be balanced.

Technical Methodology

The pilot applies a four-stage technical workflow:

1. Data Collection
   Real-time inputs such as vessel estimated time of arrival (ETA), crane availability, labor rosters, and throughput targets are fed into hybrid solvers.

2. Problem Formulation
   DQI researchers translate these operational datasets into QUBO (Quadratic Unconstrained Binary Optimization) and Job Shop Scheduling models suitable for quantum solvers.

3. Solver Execution
   Quantum-inspired algorithms and QAOA solvers are run through simulated quantum processors and annealing platforms. These solvers generate optimized berth and crane schedules.

4. Integration Testing
   Outputs are benchmarked against conventional methods, evaluating KPIs such as berth utilization, crane idle time, and total container throughput.

Pilot Outcomes: Early Insights

Although the pilot is ongoing, initial tests from mid-2022 yielded encouraging results:

• Berth optimization reduced average vessel waiting times by 6–8%, particularly during peak congestion hours.

• Crane scheduling efficiency improved by approximately 4%, ensuring more balanced workloads.

• Container flow mapping suggested new truck-stacking and sequencing strategies, aligning better with real-time berthing shifts.

While these gains may appear incremental, when scaled across an annual cycle, they translate into millions in cost savings, shorter turnaround times, and reduced greenhouse gas emissions.

Global Maritime Relevance

The Rotterdam pilot places Europe at the forefront of applying quantum computing to maritime logistics. Comparable efforts are emerging worldwide:

• Port of Singapore has collaborated with D-Wave on quantum-inspired container stacking optimization.

• Port of Los Angeles is experimenting with IoT-based berth analytics to improve ship turnaround.

• Hamburg Port Authority has invested heavily in AI-powered digital twins.

What sets Rotterdam apart is its commitment to quantum-native approaches, not just classical heuristics enhanced by AI. This positions the port as a leader in testing how next-generation computation can directly impact supply chain resilience.

Infrastructure Integration Strategy

A key strength of the Rotterdam-DQI project is its emphasis on system interoperability. The quantum optimization module is being designed to plug into existing port systems rather than replace them.

• PortXchange Synchronizer provides the real-time berth planning interface.

• Navis TOS handles crane and yard resource allocation.

• APM Terminal software manages automated guided vehicle (AGV) dispatch and cargo flow monitoring.

Quantum outputs feed into these existing frameworks, offering operators recommendation layers that enhance decision agility.

Challenges and Considerations

Despite promising results, several barriers remain:

• Hardware readiness: Today’s quantum systems lack the qubit count and stability required for full-scale deployment.

• Integration latency: Real-world port operations require sub-minute decision support, which hybrid solvers are only beginning to approach.

• Cost-benefit analysis: Large-scale adoption will depend on demonstrating long-term ROI.

To address these challenges, DQI and the Port Authority are relying on quantum-inspired solutions that emulate quantum techniques on classical infrastructure, bridging the gap until scalable hardware becomes available.

Policy and Ecosystem Impact

The pilot strengthens the Netherlands’ role in the European quantum corridor, linking Delft, Eindhoven, and Amsterdam as hubs for advanced quantum research.

It also supports broader EU initiatives:

• Smart and Green Ports Initiative, targeting emission reductions through efficiency gains.

• NextGenerationEU recovery plan, funding digital infrastructure upgrades.

• Quantum Flagship Program, prioritizing logistics and mobility as key application domains.

By piloting real-world applications, Rotterdam not only advances its own competitiveness but also shapes EU policy direction for quantum logistics.

Looking Ahead: Roadmap and Expansion

The Port of Rotterdam Authority has outlined next steps:

• Expand quantum pilots to additional terminals and inland barge hubs.

• Explore new applications, including AGV path optimization and energy-efficient scheduling.

• Establish a Quantum Logistics Sandbox to allow other European ports and carriers to test modules.

• Advocate for standardized quantum optimization protocols in global maritime logistics.

Conclusion

The June 27, 2022 pilot between the Port of Rotterdam and the Delft Quantum Institute marks a milestone in applying quantum computing to real-world logistics. Early results already indicate measurable improvements in berth scheduling, crane utilization, and container throughput.

As global trade faces growing disruptions—from pandemic aftershocks to geopolitical tensions and climate pressures—the ability to harness advanced computation for resilience and efficiency will be a defining advantage.

By embracing quantum optimization, Rotterdam is not only enhancing its own operational capacity but also shaping the future of global maritime logistics. In doing so, it signals a new era where ports evolve from being physical gateways of commerce to digital-quantum ecosystems, resilient enough to handle the uncertainties of twenty-first-century trade.

Europe’s largest and most technologically advanced port is taking its next digital leap. On June 20, 2022, the Port of Rotterdam Authority confirmed an exploratory partnership with QuTech—a joint initiative of Delft University of Technology and TNO, the Netherlands Organization for Applied Scientific Research—to test the use of quantum computing in optimizing global shipping operations.

For decades, the Port of Rotterdam has been a trailblazer in port digitalization, pioneering predictive analytics, AI-driven scheduling, and IoT-based logistics platforms. Yet even with these advances, the port’s leadership recognizes that traditional algorithms are hitting their limits in environments as complex and interdependent as modern maritime supply chains. This new initiative aims to evaluate whether quantum computing—an emerging field capable of processing combinatorial problems at unprecedented scale—can help ports manage uncertainty, congestion, and sustainability demands.

Why Rotterdam Is Betting on Quantum

Rotterdam is not just a port—it is the beating heart of European trade. Handling more than 14 million TEUs (twenty-foot equivalent units) annually and serving as a gateway to Germany, France, and beyond, its smooth operation is essential to continental supply chains. But with rising congestion, climate-driven weather volatility, and mounting pressure to decarbonize, even minor inefficiencies ripple across global networks.

Traditional berth scheduling, crane assignment, and container routing rely on linear programming or heuristic rules. These tools, while effective, become strained under variable conditions like unexpected vessel delays or sudden shifts in hinterland capacity. Quantum computing, by contrast, is designed for problems that require simultaneous optimization across thousands of variables—making it an attractive candidate for port logistics.

The Scope of the Quantum Logistics Initiative

The Port of Rotterdam’s quantum program initially focuses on three critical bottlenecks:

1. Berth Planning and Vessel Forecasting — determining which ships dock where and when, even when congestion and late arrivals occur.

2. Dynamic Container Transshipment — optimizing flows across rail, barge, and truck connections to minimize delays and reduce empty runs.

3. Energy-Aware Scheduling — aligning cargo movements and equipment usage with carbon reduction goals and electrification initiatives.

The pilot explores whether quantum algorithms can provide faster, more adaptive solutions than existing classical methods, particularly in scenarios with conflicting priorities and limited resources.

The Research Partners and Their Roles

The collaboration brings together three leading entities:

• Port of Rotterdam Authority — supplying real logistics data, defining operational problems, and benchmarking algorithmic performance.

• QuTech (TU Delft + TNO) — contributing expertise in quantum algorithm design, modeling tools, and access to quantum simulation infrastructure.

• Quantum Internet Alliance (QIA) — exploring long-term applications of secure quantum communication for port data exchanges.

This partnership underscores the Dutch ecosystem’s strength in quantum R&D, positioning Rotterdam not only as a logistics hub but also as a living laboratory for next-generation computing.

From Smart Port to Quantum Port

The Port of Rotterdam is no stranger to cutting-edge technology. Its “Port Forward” innovation agenda has already deployed digital twins of infrastructure, AI-based vessel traffic management, and IoT-driven smart cranes. Quantum computing adds another layer of sophistication.

Several quantum use cases are under evaluation:

• Quantum-Inspired Metaheuristics — improving scheduling flexibility during overlapping ship arrivals.

• Quantum Machine Learning (QML) — predicting container dwell times more accurately than classical regression models.

• Quantum Approximate Optimization Algorithm (QAOA) — finding near-optimal berth and yard crane assignments.

The first tests are being run on Dutch-hosted cloud emulators and on early-access hardware from IBM Q and Rigetti.

Early Simulation Results

Preliminary findings suggest meaningful promise. In one benchmark scenario involving berth scheduling under congestion, QAOA achieved solutions close to optimal while consuming up to 20% less computational time than classical solvers like CPLEX or Gurobi. Simulations also showed potential berth utilization improvements of 3–5%, which could translate into shorter vessel turnaround times and reduced congestion.

Though these results are still early and based on simulations, they reinforce the idea that quantum tools may deliver measurable benefits in highly constrained, data-rich environments like ports.

Building Resilience Post-Pandemic

The COVID-19 pandemic revealed how fragile global logistics can be. Rotterdam, like many ports, faced vessel backlogs, capacity mismatches, and strained hinterland connections. Combined with ongoing geopolitical tensions and climate disruptions, the urgency to adopt more resilient systems has grown.

Quantum optimization is being framed as a complement to existing digital twins and AI, not a replacement. By allowing ports to simulate a wider range of scenarios and adapt in real-time, it could become a cornerstone of resilient logistics architecture.

Strategic and Policy Context

Rotterdam’s quantum program is part of a larger European movement.

• The EU Quantum Flagship identifies logistics as a priority application area.

• The Netherlands National Agenda for Quantum Technology (NAQT) lists smart logistics as one of its five pillars.

• The Digital Transport & Logistics Forum (DTLF) encourages pilots that integrate quantum with AI, 5G, and cloud technologies.

This means Rotterdam’s initiative is not just about local efficiency—it is also about positioning Europe at the forefront of quantum-enabled logistics.

Technical Framework

The pilot is structured around a multi-layer architecture:

1. Problem Formalization — translating berth scheduling and container routing into mathematical forms suitable for quantum optimization (e.g., QUBO).

2. Algorithm Library — applying QAOA, Grover’s search, and quantum-enhanced Monte Carlo methods.

3. Simulation Backends — leveraging Qiskit and Rigetti Forest for workload emulation.

4. Integration Layer — connecting quantum outputs to Portbase, Rotterdam’s logistics information system.

If successful, these workflows could be scaled into hybrid cloud platforms that integrate classical and quantum computing seamlessly.

Expert Insights

Dr. Stephanie Wehner, Director at QuTech, explained:
“We’re just scratching the surface of what quantum can do in logistics. Ports are a perfect testing ground—dynamic, intermodal, and data-rich.”

Jeroen van der Hout, Lead Architect of Rotterdam’s Digital Twin initiative, added:
“Quantum optimization is a natural extension of our digital strategy. The complexity of shipping demands new tools, and quantum gives us a chance to stay ahead.”

Outlook and Next Steps

Looking ahead, the Port of Rotterdam Authority intends to:

• Expand quantum research into energy optimization for port electrification.

• Run pilot programs with terminal operators starting in 2023.

• Explore quantum-secure communication channels via Quantum Delta NL.

• Establish a “quantum sandbox” in its Digital Port Lab to test applications with broader industry partners.

These initiatives underline Rotterdam’s belief that quantum is not a futuristic luxury, but a long-term necessity for competitive ports.

Conclusion

The Port of Rotterdam’s partnership with QuTech and Delft University marks a milestone in the convergence of quantum computing and maritime logistics. By testing quantum algorithms in berth scheduling, intermodal routing, and carbon-aware operations, Rotterdam is positioning itself as the world’s first true “quantum port.”

While practical deployment may still be years away, the port’s proactive stance demonstrates that quantum computing is shifting from abstract research into applied logistics. If successful, the lessons learned in Rotterdam could ripple across global trade, setting a precedent for how the world’s busiest ports embrace the quantum era.

Quantum Enters Automotive Supply Chains

The automotive industry sits at the nexus of global logistics complexity. Automakers operate sprawling supply chains with thousands of suppliers, highly synchronized assembly lines, and millions of parts moving across continents daily. Any disruption—whether from geopolitical instability, pandemic restrictions, or natural disasters—can ripple across production schedules and dealership availability.

In this environment, optimization tools are indispensable. Yet even advanced classical solvers often struggle with the sheer scale and combinatorial intensity of automotive logistics problems. On June 13, 2022, Palo Alto–based QC Ware announced a partnership with BMW Group and the U.S. Department of Energy’s Argonne National Laboratory to test whether quantum computing can provide an edge.

The pilot focused on integrating hybrid quantum algorithms with real-world automotive logistics data, addressing challenges such as route planning for parts delivery, scheduling at distribution warehouses, and inventory buffering to avoid costly shortages.

The Partners and Their Roles

The project brought together three distinct players:

• QC Ware: A leading quantum software company specializing in hybrid optimization solvers. It provided the quantum algorithms and a platform for interfacing logistics data with quantum backends.

• BMW Group: Supplied anonymized logistics data from its U.S. operations, particularly the Spartanburg plant in South Carolina, BMW’s largest global production facility. Logistics flows included spare parts distribution and regional freight management networks.

• Argonne National Laboratory: Contributed expertise in computational modeling, benchmarking, and verification of algorithmic performance. As part of the U.S. DOE, Argonne helped ensure scientific rigor while aligning the project with national priorities for quantum technology.

This triad exemplifies a public-private partnership model: industry players testing cutting-edge science in real-world contexts, with federal research labs bridging the gap.

Pilot Scope and Objectives

The project addressed three main categories of automotive logistics:

1. Route Optimization: Mapping vehicle routing problems (VRPs) to quantum optimization models, focusing on spare parts and intermodal freight between plants, ports, and warehouses.

2. Warehouse Scheduling: Exploring docking window allocations, shipment batching, and sequencing to maximize throughput and reduce idle time.

3. Inventory Management: Studying container load planning and buffering strategies to balance costs with supply resilience.

Each of these problems is combinatorial in nature, requiring algorithms to evaluate vast numbers of possibilities quickly. Classical solvers like Gurobi and CPLEX are industry standards, but hybrid approaches—combining quantum and classical computing—promise to converge faster or identify novel solutions in some contexts.

Quantum Algorithms in Play

QC Ware deployed several key techniques:

• Quantum Approximate Optimization Algorithm (QAOA): Applied to VRPs, this algorithm seeks near-optimal routing solutions with fewer computational steps.

• Quantum-enhanced Monte Carlo Sampling: Used to model schedule flexibility and probabilistic disruptions such as late shipments or sudden demand changes.

• Hybrid Quantum-Classical Solvers: Leveraged QC Ware’s proprietary platform to decide dynamically whether a given subproblem should run on classical hardware, a quantum simulator, or a live quantum processor.

The algorithms were executed both on simulators and on real quantum backends, including Rigetti’s superconducting processors and IonQ’s trapped-ion systems, accessible via the cloud.

Early Results

Though exploratory, the pilot delivered promising outcomes:

• Quantum solvers matched classical accuracy on smaller problem sets, confirming their validity.

• Hybrid solvers converged faster in constrained scheduling scenarios, where docking times and multiple routing constraints overlapped.

• Simulations indicated a 7–10% improvement in container utilization, a meaningful gain in an industry where shipping costs are a major expense.

Argonne researchers emphasized that quantum tools are not yet replacing classical optimization engines but can supplement them effectively—particularly in scenarios where flexibility and rapid re-optimization are critical.

Strategic Significance

For BMW, this project continues a broader exploration of quantum computing that began in 2021, when the company tested quantum chemistry models for battery R&D. The logistics pilot reflects BMW’s intent to apply quantum to both product innovation and operational resilience.

For QC Ware, the collaboration demonstrates credibility in a high-stakes industrial context. Bridging cutting-edge quantum algorithms with automotive-scale logistics establishes the company as a serious player in applied quantum computing.

For the U.S. Department of Energy, the pilot underscores the relevance of federally funded quantum research to real-world industrial challenges—enhancing both national competitiveness and supply chain resilience.

Policy and Market Context

This initiative is set against a backdrop of heightened U.S. federal investment in quantum technologies. The National Quantum Initiative Act and DOE’s Office of Advanced Scientific Computing Research both emphasize applied use cases with direct industrial impact.

Meanwhile, global supply chain disruptions following the COVID-19 pandemic and geopolitical tensions have accelerated demand for adaptive planning tools. Companies like BMW are now prioritizing resilience alongside efficiency, making experimental approaches like quantum optimization attractive.

Technical Architecture

The solution architecture unfolded in four layers:

1. Data Ingestion: BMW’s logistics data—shipment manifests, warehouse schedules, and routing maps—was formatted into optimization-ready inputs.

2. Problem Mapping: Constraints were encoded into Quadratic Unconstrained Binary Optimization (QUBO) forms for quantum algorithms and into classical VRP benchmarks for comparison.

3. Hybrid Execution: QC Ware’s platform dynamically selected the best solver for each subproblem—classical, quantum, or hybrid.

4. Evaluation Metrics: Key performance indicators included route efficiency, computational time, container utilization, and load balancing effectiveness.

Insights from the Field

BMW’s logistics engineers reported noticeable improvements in simulation responsiveness and multi-variable optimization. Faster solver convergence gave them more flexibility when testing alternate freight scenarios.

Dr. Alan Ho, VP of Strategy at QC Ware, framed the pilot this way:
"The logistics industry is one of the ripest fields for quantum acceleration—complex, data-rich, and optimization-heavy. This pilot is a glimpse into how hybrid models can begin solving real-world problems today."

Roadmap for Expansion

Following the U.S. pilot, QC Ware announced intentions to:

• Expand trials to BMW’s European and Asian supply chains.

• Partner with additional OEMs and global freight carriers.

• Deepen collaborations with hardware providers like IonQ, Rigetti, and Quantinuum.

• Release a logistics-focused quantum SaaS module in 2023, targeting industries beyond automotive, including aerospace and consumer goods.

BMW and Argonne are also considering co-funded R&D into quantum-enhanced inventory control, port logistics, and low-emissions freight routing.

Conclusion

The June 13, 2022 announcement of QC Ware’s pilot with BMW and Argonne marks a pivotal step for applied quantum logistics. While results are still preliminary, the collaboration demonstrates that hybrid quantum-classical algorithms can already provide measurable improvements in automotive supply chains.

For automakers, quantum optimization represents a potential new toolkit for managing complexity and uncertainty. For quantum startups, projects like this are proof points that the technology is moving from academic curiosity toward industrial relevance. And for national research institutions, it validates investments in quantum infrastructure as vital for economic and strategic resilience.

As QC Ware, BMW, and Argonne continue their roadmap, the broader industry will be watching closely. The future of logistics may well hinge on the successful integration of quantum computing into supply chain decision-making—bringing new levels of efficiency, flexibility, and resilience to one of the world’s most complex industries.

Quantum Tracks: Applying Quantum Algorithms to Rail Logistics

On June 6, 2022, Canadian National Railway (CN) and Toronto-based quantum computing firm Xanadu officially launched a groundbreaking collaboration to explore how quantum optimization could reshape freight scheduling and capacity planning in the rail industry. The announcement, made in Toronto, marks a historic milestone as Canada’s first railway-sector quantum logistics pilot. It not only underscores the growing momentum of the country’s quantum ecosystem but also signals the beginning of a transformative journey in North American supply chain orchestration.

CN, one of North America’s most extensive rail operators, manages a network of over 30,000 kilometers spanning Canada and parts of the United States. This immense logistical footprint requires coordination of thousands of freight cars, hundreds of locomotives, and multiple intermodal terminals, all under tight scheduling constraints. Traditionally, these challenges have been addressed with classical optimization methods. However, as the supply chain faces increasing pressures from congestion, environmental mandates, and evolving customer expectations, CN is turning to quantum computing as a next-generation solution.

Xanadu, founded in 2016, has emerged as a global leader in photonic quantum computing. Its open-source software platform, PennyLane, is already widely adopted by researchers and developers building hybrid quantum-classical applications. By partnering with CN, Xanadu is positioning itself as a pioneer in bringing quantum technologies directly into heavy industries like rail transportation.

Project Scope and Research Goals

The joint initiative between CN and Xanadu aims to tackle three of the most pressing problems in freight rail logistics:

1. Train departure scheduling – Determining optimal departure times and track allocation when yard and mainline availability are limited.

2. Intermodal cargo flow optimization – Coordinating container transfers between rail, trucking fleets, and port facilities to ensure smooth cargo handoffs.

3. Freight car allocation and sequencing – Assigning cars to specific trains while minimizing idle time and maximizing energy efficiency.

These tasks are highly combinatorial in nature, meaning that as variables scale, the number of possible solutions grows exponentially. For instance, even a mid-sized scheduling problem can involve billions of possible configurations, making them extremely challenging for classical solvers.

The CN–Xanadu team is exploring the use of Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolvers (VQE) to tackle these problems. While today’s quantum hardware is not yet at large-scale fault tolerance, hybrid approaches allow partial solutions to be generated by quantum processors and refined with classical computing.

Key Partners and Technical Contributions

The project is structured as a multi-stakeholder collaboration involving industry, academia, and quantum technology providers:

• Canadian National Railway (CN): Provides anonymized historical and real-time freight data, operational constraints, and logistics expertise.

• Xanadu: Supplies quantum hardware through its photonic quantum processors, as well as the PennyLane software platform for hybrid algorithm design.

• University of Toronto: Contributes academic expertise, validation frameworks, and graduate-level research talent to strengthen algorithmic testing.

By combining operational knowledge with quantum expertise, the partnership aims to produce solutions that are not only scientifically innovative but also directly applicable to real-world rail logistics.

Why Rail Logistics Needs Quantum Help

Rail freight scheduling is notoriously complex. Operators must coordinate track time, locomotive power, train crew availability, and intermodal interfaces. Disruptions—such as weather delays, maintenance outages, or unexpected surges in cargo—further complicate planning.

Challenges include:

• Freight car assignment: Efficiently allocating thousands of cars without creating empty hauls.

• Yard management: Avoiding bottlenecks where trains must be broken down and reassembled.

• Network synchronization: Ensuring arrival and departure times align across multiple hubs.

Traditional heuristics and brute-force solvers often fail at large scale or take too long to adapt when disruptions occur. Quantum algorithms, however, can explore solution spaces more effectively by leveraging quantum mechanical properties like superposition. This could allow rail operators to find better schedules, faster, and more reliably than before.

Early Experiments and Benchmarks

As part of their initial research, CN and Xanadu modeled a simplified corridor scheduling problem with constraints such as:

• Limited siding capacity

• Overlapping train paths

• Car-type-specific dwell time targets

By applying QAOA through PennyLane and running experiments on Xanadu’s X-Series photonic quantum processors, the team achieved:

• 10% reduction in average train wait times compared to classical heuristics

• Improved yard utilization during simulated peak congestion

• Faster rescheduling when disruptions like delays or equipment faults were introduced

While these results are early-stage and based on scaled-down models, they provide meaningful evidence that quantum optimization can deliver advantages over legacy methods.

Strategic Significance for Canada’s Quantum Ecosystem

The CN–Xanadu partnership is not occurring in isolation. Canada has made substantial investments in quantum research, highlighted by the federal National Quantum Strategy launched in early 2022. Initiatives such as the Quantum Valley Ideas Lab in Waterloo and partnerships across universities have positioned Canada as a global hub for quantum development.

By bringing quantum into the rail sector, CN is not only solving pressing operational challenges but also anchoring industrial applications of quantum in Canada. This reinforces the country’s ambition to lead in both scientific research and commercialization.

As CN’s Chief Innovation Officer, Michelle Novak, remarked during the announcement:
“Rail logistics is undergoing digital transformation. We believe quantum optimization will be one of the pillars of intelligent supply chain orchestration in the next decade.”

Xanadu’s Photonic Edge

Unlike many competitors pursuing superconducting or trapped-ion systems, Xanadu is developing photonic quantum computers, which use particles of light (photons) to perform quantum operations. These systems offer several advantages:

• Operate at room temperature, unlike cryogenic systems

• Modular scalability for large quantum networks

• Natural integration with fiber-optic and cloud infrastructure

For this project, Xanadu deployed a 24-qumode version of its X-Series processor, allowing CN’s logistics problems to be modeled directly in variational circuits. The system’s API access enabled seamless integration into CN’s existing Python-based logistics workflows.

Broader Industry Implications

The significance of this research extends beyond Canada’s borders. Globally, railways are seen as ideal candidates for quantum optimization due to:

• Fixed infrastructure networks, reducing variables compared to road freight

• High-volume, high-density scheduling needs

• Long planning horizons, compatible with hybrid batch optimization models

As more railways move toward precision scheduled railroading (PSR) and multimodal orchestration, the ability to adapt rapidly to disruptions will be crucial. Quantum tools can provide operators with decision-support systems that are both faster and more resilient.

Roadmap and Next Steps

The CN–Xanadu partnership has outlined a phased roadmap:

• Phase 1 (Q2–Q4 2022): Model development and simulation with real CN datasets

• Phase 2 (2023): Prototype integration with CN’s yard and scheduling tools

• Phase 3 (2024+): Deployment testing on larger, next-generation quantum hardware

The partners are also considering publishing open benchmarks for rail optimization, inviting collaboration across the logistics and quantum communities. Such benchmarks would accelerate progress by creating standard problem sets for researchers and technology developers.

Conclusion

The June 2022 launch of CN and Xanadu’s quantum freight scheduling research represents a milestone not just for Canada but for the global logistics sector. For CN, it opens the possibility of a new era of intelligent, adaptive scheduling that improves efficiency, reduces delays, and strengthens resilience across its network. For Xanadu, it provides an industrial proving ground for its photonic quantum technology.

Most importantly, the initiative demonstrates how quantum computing can move beyond theory and research labs into real-world applications. As railways, ports, and logistics operators worldwide face mounting pressures, quantum optimization may soon become a defining tool of next-generation supply chains.

By combining Canada’s strengths in both logistics and quantum research, CN and Xanadu have positioned themselves at the forefront of a technological revolution—one that could reshape how freight moves across continents in the years to come.

Seaports remain some of the most critical and congested nodes in the global supply chain, handling billions of tons of goods every year. In Europe, the Port of Valencia stands out not only for its scale — consistently ranking among the continent’s busiest — but also for its ongoing commitment to digital transformation. On May 26, 2022, Valencia became one of the first ports in the world to successfully test quantum-inspired optimization inside a digital twin, signaling a breakthrough in how container terminals can operate in the 21st century.

This pilot, launched in partnership with Multiverse Computing, aimed to solve some of the most stubborn bottlenecks in port management. By embedding quantum-inspired algorithms into a real-time virtual replica of port workflows, Valencia demonstrated how future-ready optimization methods can reshape global trade efficiency and sustainability.

Quantum Logistics Meets the Shipping Industry

The shipping sector faces mounting challenges: rising cargo volumes, tighter climate regulations, and unpredictable disruptions such as COVID-era backlogs. Ports, already pressured by these demands, need innovative solutions to stay resilient.

The Port of Valencia, already known as a smart port pioneer, took a bold step forward by embracing quantum-inspired computing. The pilot was conducted through the collaboration of three organizations:

• Fundación Valenciaport (ValenciaPort Foundation) – the innovation hub driving modernization projects.

• Multiverse Computing – a leading quantum software company based in Spain, specializing in optimization algorithms.

• InnovaQuantum – a joint research platform focused on advancing logistics applications.

Together, these partners launched a digital twin of Valencia’s port ecosystem and enhanced it with quantum optimization capabilities, specifically targeting real-time scheduling and container traffic flow.

Inside the Quantum Digital Twin Pilot

The project focused on three critical challenges in container logistics:

1. Yard crane scheduling – determining which cranes should move containers and at what time, balancing workloads across equipment.

2. Vessel berthing optimization – deciding which ship docks at which berth to minimize waiting times and maximize port throughput.

3. Container slotting and stacking – managing how containers are placed and reshuffled to reduce unnecessary moves.

At the core of this effort was Multiverse Computing’s Singularity platform, which supports hybrid quantum-classical optimization approaches.

Technical Highlights of the Pilot:

• Optimization Encoding: Logistics challenges were modeled as Quadratic Unconstrained Binary Optimization (QUBO) problems, a standard framework in quantum optimization.

• Algorithms: Tensor networks and quantum-inspired heuristics ran on classical hardware (GPUs) but exploited quantum principles for better search.

• Speed: Solutions were generated 30–50% faster than legacy optimization systems, reducing computational bottlenecks.

• Integration: APIs linked the system directly to the port’s operational data streams, ensuring recommendations could be tested in real time.

The trial demonstrated measurable benefits: crane scheduling conflicts dropped by up to 17%, and container reshuffling decreased by 12%. These gains translated into smoother operations and fewer delays across terminal activities.

Why Quantum-Inspired Optimization Matters

Quantum-inspired optimization does not rely on fault-tolerant quantum computers — machines that remain years away from commercial deployment. Instead, it uses algorithms modeled on quantum mechanics but runs on today’s classical infrastructure.

For Valencia, this approach offered three immediate advantages:

• Scalability – able to model thousands of interdependent decisions in real-world conditions.

• Adaptability – capable of recalculating optimal schedules in seconds when ships are delayed or equipment breaks down.

• Sustainability – reduced crane idle time and vessel waiting directly translate into lower fuel consumption and emissions.

By using quantum-inspired techniques, Valencia avoided the hardware limitations of early quantum processors while still benefiting from advanced mathematical models that extend beyond conventional optimization methods.

Strategic Relevance for Ports and Supply Chains

The trial was more than a technical success — it carried significant strategic weight. Ports globally are seeking to strengthen resilience after years of disruptions. For Valencia, the quantum digital twin became a tool for both efficiency and sustainability.

Key goals included:

• Cutting emissions through more efficient use of cranes and reduced vessel waiting times.

• Increasing throughput capacity to accommodate projected growth in global shipping volumes.

• Creating a predictive, flexible logistics framework able to withstand unexpected shocks such as extreme weather or sudden demand surges.

The initiative also aligned with broader policy priorities. Spain’s Ministry of Transport has championed intelligent port operations, and the European Union’s Horizon Europe program has emphasized green logistics as central to climate transition.

Valencia’s Position as a Smart Port Leader

The Port of Valencia has already deployed a variety of digital innovations:

• IoT networks to track assets and monitor environmental conditions.

• AI-driven scheduling for truck and gate management.

• Renewable energy systems, including solar arrays, to reduce carbon footprint.

• 5G infrastructure to support autonomous machinery and real-time data exchange.

Adding quantum-inspired optimization to this foundation allowed the port to extend its technological leadership. The project showed how a port can evolve from digital to quantum-enhanced operations — a trajectory other global hubs are closely watching.

Multiverse Computing’s Role in Logistics Transformation

Founded in San Sebastián, Spain, Multiverse Computing has quickly become one of the most visible players in applying quantum techniques to real-world industries. Beyond logistics, the company has projects in:

• Finance – portfolio optimization with banks like BBVA and Crédit Agricole.

• Energy – balancing renewable grids with Iberdrola.

• Manufacturing – predictive maintenance with Basque Country factories.

• Mobility – smart city traffic systems in Barcelona and Paris.

For logistics, the Valencia pilot was a natural extension, positioning Multiverse as a critical enabler of quantum-ready supply chains.

Roadmap Toward Scaled Deployment

Following the May 2022 trial, Fundación Valenciaport and Multiverse Computing defined a multi-stage roadmap:

• Expansion of the digital twin – covering rail links, road access, and shipyards in addition to container terminals.

• Integration of predictive analytics – factoring in vessel delays, weather events, and equipment maintenance schedules.

• Embedding into the Port Community System (PCS) – ensuring that terminal operators, shipping lines, and freight forwarders can access and benefit from quantum-optimized decisions.

• European collaboration – launching a quantum logistics working group under ALICE (Alliance for Logistics Innovation through Collaboration in Europe) to share best practices.

These steps mark a pathway toward a fully integrated, quantum-enhanced logistics ecosystem.

Global Implications for Quantum Logistics

The success of the Port of Valencia pilot sends an important message: quantum logistics is no longer theoretical. Ports in Rotterdam, Hamburg, and Singapore are already investigating similar digital twin initiatives, with quantum-inspired methods at the forefront.

The broader implication is that quantum optimization is moving from pilot phase to operational readiness. As ports integrate these tools with AI, IoT, and 5G, the performance gap between early adopters and laggards will widen significantly.

Conclusion

The Port of Valencia’s May 26, 2022 quantum digital twin trial represents a turning point for maritime logistics. By combining digital twins, live data integration, and quantum-inspired algorithms, the project improved crane scheduling, vessel berthing, and container flows — all while reducing emissions and boosting resilience.

Valencia’s success demonstrates how seaports, often seen as traditional and infrastructure-heavy, can lead the charge into next-generation computing. For the global shipping industry, the pilot is proof that quantum-inspired optimization is not just a research curiosity but a practical tool reshaping real-world supply chains today.

On May 23, 2022, Toshiba Digital Solutions and All Nippon Airways (ANA) jointly announced the successful deployment of a quantum-inspired logistics optimization platform designed to schedule and route autonomous drones in rural Japan. The initiative represents one of the most tangible applications of advanced computing in logistics to date, solving pressing delivery challenges in regions where traditional infrastructure is expensive, inefficient, or outright inaccessible.

Tackling Japan’s Rural Logistics Challenge

Japan’s logistics sector has long faced an acute challenge in servicing its rural and aging population. With more than 28% of the country’s citizens aged 65 or older, demand for reliable delivery of daily necessities and medical supplies is increasing. Yet in mountainous, coastal, and sparsely populated regions, traditional truck-based delivery is expensive and often delayed by weather or limited infrastructure.

ANA, which has been developing autonomous drone delivery as a core component of its logistics vision, quickly discovered that optimizing drone operations was far from simple. Constraints such as payload limits, battery range, wind conditions, and shifting demand meant that simple heuristic scheduling methods fell short. The airline needed a system capable of managing combinatorial optimization in real time, balancing multiple variables while ensuring reliability.

Rather than wait for commercial-scale quantum computers, ANA partnered with Toshiba to use the Simulated Bifurcation Machine (SBM)—a classical computing platform inspired by quantum dynamics.

Toshiba’s Simulated Bifurcation Approach

The SBM is not a true quantum computer. Instead, it mimics aspects of quantum bifurcation dynamics to solve optimization problems. At its core, the system translates logistics scheduling challenges into Quadratic Unconstrained Binary Optimization (QUBO) problems.

For ANA’s drones, this meant encoding data such as:

• Flight paths and distances

• Battery recharge or swap schedules

• Delivery windows and package weights

• Weather inputs and emergency rerouting needs

The SBM then simulated the system’s evolution, quickly converging on low-energy configurations that represented near-optimal solutions.

Unlike gate-based quantum processors or annealers that require cryogenic cooling and highly sensitive qubits, Toshiba’s SBM runs on conventional hardware but is optimized to handle up to 100,000 variables in real-world applications.

Pilot Project in Hiroshima Prefecture

The first test deployment began in Fukuyama City, Hiroshima Prefecture, in early May 2022. ANA operated a fleet of 12 drones across five rural delivery zones, serving primarily elderly residents who required medical deliveries and daily goods.

The system recalculated schedules every 10 minutes, adjusting to new orders, wind speed changes, and battery depletion.

Key results from the three-week pilot included:

• 13% reduction in total flight time compared to baseline planning

• 11% improvement in on-time delivery performance

• Fewer canceled flights, due to proactive rerouting under weather disruptions

• Improved drone utilization efficiency, ensuring balanced workload distribution

For ANA, these numbers carried significant weight. In logistics networks, even single-digit efficiency improvements translate into cost savings, increased coverage, and more reliable service for customers.

Integration Into ANA’s Broader Vision

ANA has announced its ambition to scale drone logistics across rural and disaster-prone regions of Japan by 2025. The roadmap envisions:

• Establishing more than 100 drone bases across remote areas

• Using real-time SBM optimization to coordinate fleets

• Integrating optimization with 5G connectivity, AI-based weather prediction, and smart city infrastructure

The Hiroshima pilot demonstrated that quantum-inspired systems can serve as the operational brain of this vision, enabling drones to adapt dynamically while minimizing human oversight.

Global Context: Quantum-Inspired Optimization on the Rise

Toshiba’s achievement fits within a broader global movement toward quantum-inspired optimization tools. In parallel with ANA’s pilot, other initiatives gained traction in 2022:

• Fujitsu’s Digital Annealer was used by Japan Post and Mercedes-Benz for logistics and factory layout planning.

• NEC’s vector annealer targeted port container management and congestion reduction.

• Microsoft’s Azure Quantum-Inspired Optimization Engine piloted warehouse layout improvements in the United States.

Japan’s aggressive adoption reflects its pressing demographic and geographic logistics challenges, alongside strong public-private partnerships supported by government programs like the Moonshot R&D initiative, which targets fully autonomous logistics systems by 2030.

Technical Deep Dive: SBM in Action

The SBM’s logistics optimization workflow involved several key steps:

1. Data Encoding – Customer orders, drone capacity, and delivery zones were transformed into binary variables.

2. QUBO Formulation – Constraints such as battery range, payload, and delivery windows were mapped as penalties in a cost function.

3. Bifurcation Simulation – The SBM simulated the system’s progression toward stable states, rapidly identifying near-optimal solutions.

4. Schedule Deployment – Optimal schedules were sent to ANA’s drone control systems for execution.

5. Continuous Feedback Loop – Every 10 minutes, the system re-optimized based on real-world conditions.

This rapid refresh rate was crucial in adapting to sudden demand surges, weather shifts, or drone malfunctions, ensuring resilient service.

Quantum-Inspired vs Quantum Hardware

Although the SBM borrows concepts from quantum physics, it is fundamentally classical. Advantages include:

• Operates on existing server infrastructure

• Scales to tens of thousands of variables

• Free from quantum decoherence and noise issues

However, limitations remain:

• No true quantum speedup

• Requires reformulating problems into QUBO form

• Dependent on heuristic fine-tuning for accuracy

For logistics operators, though, these trade-offs are worthwhile. What matters most is the ability to generate fast, adaptive, near-optimal solutions—qualities the SBM delivers today.

Broader Implications for Quantum Logistics

The ANA–Toshiba deployment demonstrates that quantum-inspired computing is not just theoretical but operationally viable for logistics today. Lessons from this pilot highlight several industry-wide implications:

• Last-mile delivery and drone routing are prime candidates for optimization technology.

• Quantum-inspired tools serve as a bridge, preparing companies for a future where full quantum hardware may unlock additional gains.

• Public-private collaborations accelerate innovation, ensuring solutions are tested in real-world environments rather than confined to labs.

For Japan, this project strengthens its positioning as a global leader in smart mobility and next-generation logistics, while addressing the immediate needs of rural communities.

Conclusion

The launch of Toshiba and ANA’s quantum-inspired drone logistics routing system in May 2022 underscores the practical value of advanced optimization today. By deploying the Simulated Bifurcation Machine to manage autonomous drone fleets, ANA improved delivery efficiency, resilience, and adaptability in rural Japan—an achievement that demonstrates how cutting-edge computational models can solve deeply human challenges.

As ANA scales this program toward nationwide coverage, and as other global players adopt similar systems, the logistics sector is entering a new phase where quantum-inspired tools complement existing AI and automation strategies. The Hiroshima pilot may be remembered as one of the earliest and clearest proofs that the future of logistics optimization has already begun.

When Amazon Web Services (AWS) and Rigetti Computing jointly revealed their quantum logistics pilot on May 16, 2022, it marked more than a technical collaboration—it represented a strategic move into the future of supply chain management. For a company that delivers millions of packages daily across continents, even fractional efficiency gains can translate into hundreds of millions of dollars saved annually. Amazon’s announcement underscored a central thesis: quantum computing, though still in its noisy intermediate-scale stage, is beginning to prove valuable in real-world logistics.

Quantum’s Role in Amazon’s Logistics Strategy

Amazon’s global logistics network is one of the largest and most complex on Earth. From massive fulfillment centers in North America to micro-warehouses in European cities, every shipment is influenced by a web of constraints: fluctuating inventory, real-time customer demand, weather, traffic conditions, and last-mile delivery challenges. Traditional optimization methods have pushed to their computational limits in addressing these factors.

Amazon has long invested in artificial intelligence, machine learning, and advanced automation to fine-tune logistics. But as the dimensionality of optimization problems grows—particularly in last-mile delivery and dynamic inventory allocation—the limitations of classical solvers become evident. This reality drove Amazon’s quantum division, under AWS’s Center for Quantum Networking, to collaborate with Rigetti Computing, a leader in superconducting quantum processors.

The partnership leverages AWS Braket, Amazon’s quantum computing cloud service, as the platform for experimentation. Through it, Amazon engineers accessed Rigetti’s Aspen-M-2 80-qubit processor to test hybrid workflows that combined classical pre- and post-processing with quantum subproblem solving.

The Problem Set: Tackling Core Supply Chain Challenges

Amazon’s testbed focused on problems central to its logistics empire, each reformulated as Quadratic Unconstrained Binary Optimization (QUBO) models to suit quantum processing. Key areas included:

• Last-mile route optimization: Testing variants of the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) under real-world constraints such as delivery time windows and traffic uncertainty.

• Dynamic warehouse slotting: Determining optimal bin assignments in constrained warehouse environments where time, size, and turnover constraints overlap.

• Demand-aware dispatching: Applying predictive modeling to align outbound delivery schedules with forecasted demand surges.

• Buffer inventory planning: Balancing stock levels in fulfillment centers to minimize shortages while avoiding overstocking.

Each problem was structured to evaluate how quantum-enhanced algorithms could complement or outperform classical heuristics.

The Quantum Stack: Rigetti Meets AWS Braket

The program integrated several technical layers:

• Quantum hardware: Rigetti Aspen-M-2 80-qubit superconducting processor.

• Algorithmic frameworks: Quantum Approximate Optimization Algorithm (QAOA) variants for combinatorial optimization.

• Hybrid orchestration: AWS Braket Hybrid Jobs API to coordinate workloads between Rigetti’s quantum processor and classical GPU clusters.

• Machine learning augmentation: QUBO models refined using ML to better encode problem constraints.

This hybrid model ensured that while quantum systems explored solution landscapes, classical processors verified and refined outputs, creating a feedback loop between the two computational domains.

Early Outcomes and Insights

The pilot produced tangible, if modest, improvements. Among the reported results:

• Warehouse efficiency: Up to 9% improvement in bin slotting efficiency in constrained layouts, reducing wasted space and movement.

• Route optimization: Delivery routes saw 5–7% distance reductions in small-scale batch simulations.

• Resilience under disruption: Improved adaptability when factoring unexpected events like weather delays or labor shortages.

At Amazon’s scale, even single-digit efficiency gains are transformative. A 1% reduction in delivery miles, for example, could save the company tens of millions of dollars annually in fuel, labor, and emissions.

Strategic Goals: Beyond Cost Savings

Amazon’s interest in quantum logistics is not limited to cost efficiency. Strategic objectives include:

• Quantum readiness: Training logistics teams to integrate quantum tools as hardware matures.

• Hybrid orchestration mastery: Understanding how quantum and classical computing can co-exist in high-speed, high-stakes logistics environments.

• Competitive positioning: Staying ahead of rivals such as Walmart and Alibaba, who are similarly investing in AI and logistics optimization technologies.

One senior Amazon scientist framed the outlook clearly: “Quantum won’t replace classical logistics tools overnight, but we expect it to unlock new efficiencies in areas where classical solvers struggle—especially under uncertainty.”

Global Implications and Industry Context

Amazon’s move places U.S. tech firms firmly within the growing global push for quantum-enabled supply chain resilience. Similar efforts include:

• Europe: The QuLog initiative, led by Airbus and BMW, testing quantum logistics pilots.

• Asia: Baidu exploring quantum vehicle routing trials in Chinese megacities.

• Public sector: U.S. National Science Foundation (NSF) and Department of Energy (DOE) investing in supply chain resilience powered by emerging technologies.

Amazon’s differentiator is scale. Unlike smaller pilots, Amazon can validate or discard quantum methods rapidly, given its enormous real-time logistics datasets.

Technical Architecture Snapshot

The architecture followed a four-phase pipeline:

1. Data preprocessing: Fulfillment and delivery data parsed into constraint graphs.

2. QUBO modeling: Constraints encoded into binary variables with penalties for violations.

3. Hybrid execution: Quantum subproblems executed on Rigetti hardware; larger heuristics run on AWS classical clusters.

4. Post-processing: Results validated against simulation environments to ensure real-world feasibility.

Risks and Limitations

Despite encouraging results, Amazon acknowledged limitations:

• Hardware constraints: Limited qubit fidelity and coherence times restrict problem size.

• Comparative performance: Classical solvers still outperform quantum systems in most real-time scenarios.

• Talent shortage: Expertise in quantum optimization remains scarce, creating a bottleneck for adoption.

Yet the company views these as short-term hurdles on a longer quantum-readiness journey.

Road Ahead

Amazon and Rigetti outlined several next steps following the May 2022 announcement:

• Scaling up: Expanding problem size as hardware evolves toward 256+ qubits.

• Deeper AI integration: Incorporating Amazon SageMaker machine learning models into hybrid workflows.

• Quantum security: Exploring quantum cryptography applications for secure logistics channels.

• Global pilots: Extending trials to cross-border customs optimization and port logistics in Europe and Asia.

Conclusion

Amazon’s collaboration with Rigetti Computing signals a pivotal step toward quantum-enhanced logistics. By applying superconducting quantum processors to complex optimization problems, the company demonstrated measurable, real-world improvements in routing and warehouse efficiency. While current quantum systems remain limited, the pilot establishes a framework for future logistics solutions that integrate hybrid computation at scale.

For Amazon, quantum exploration is not simply about efficiency—it is about maintaining its leadership in global logistics innovation. The May 2022 announcement demonstrates that quantum is moving from theory into pilot-tested reality, setting the stage for the next wave of intelligent, resilient, and sustainable supply chain systems.

BMW Group and Quantinuum announced on May 12, 2022 that they had successfully conducted a proof-of-concept applying quantum computing to one of the most critical logistical challenges in modern industry: optimizing routes for parts deliveries across BMW’s European supply chain. By harnessing trapped-ion quantum processors, the project demonstrated early yet meaningful improvements in solving variants of the Vehicle Routing Problem (VRP), achieving efficiency gains over classical optimization methods.

This collaboration marks a significant inflection point for both the automotive and quantum computing industries. For BMW, the trial aligns with its broader digital logistics strategy and its commitment to sustainability. For Quantinuum, it represents one of the first concrete demonstrations of how trapped-ion quantum hardware can produce measurable benefits in a real-world industrial workflow.

Why BMW Is Turning to Quantum Optimization

BMW’s manufacturing and supply operations are among the most intricate in Europe. The company operates a sprawling network of factories, suppliers, and warehouses, with thousands of individual parts flowing between them daily. Ensuring timely deliveries while minimizing costs and emissions requires solving complex optimization problems.

The Vehicle Routing Problem (VRP) lies at the heart of these challenges. VRP involves determining the most efficient routes for a fleet of vehicles to deliver goods across multiple locations under constraints such as delivery time windows, vehicle capacities, road restrictions, and unexpected disruptions. Classical algorithms—including heuristics and metaheuristics—perform well for smaller or structured cases. However, when scaled to large real-world networks, especially with dynamic factors, these methods struggle against the combinatorial explosion of possible solutions.

Quantum computing offers a potential way forward. By leveraging superposition and entanglement, quantum systems can evaluate a wider diversity of potential solutions, potentially escaping the local minima that often trap classical solvers.

Inside the BMW–Quantinuum Proof-of-Concept

The May 2022 trial was conducted within BMW’s European logistics framework, focusing on routes across southern Germany, where the company manages some of its most active part flows.

The challenge was formulated as a VRP variant incorporating service time windows and vehicle constraints. To tackle it, BMW and Quantinuum implemented a hybrid classical-quantum approach that allowed today’s limited quantum hardware to contribute practically.

Key technical components included:

• Quantum Hardware: Quantinuum’s H1-1 trapped-ion quantum processor, known for its high gate fidelity and all-to-all connectivity.

• Problem Encoding: A Quadratic Unconstrained Binary Optimization (QUBO) framework was used to translate routes and constraints into a quantum-compatible form.

• Hybrid Workflow: Classical pre-processing generated initial candidate solutions, which were then passed to the quantum processor for further exploration.

• Post-Processing: Classical algorithms refined the quantum-suggested solutions, ensuring compliance with operational constraints.

The test scenarios spanned 5 to 15 delivery points, within the feasible scope of current trapped-ion hardware. Even at this modest scale, quantum-enhanced solutions delivered 3–6% improvements in efficiency compared to purely classical heuristics.

Why This Trial Matters

Though small in scope, the proof-of-concept has wide implications. First, it shows that quantum computing can already provide tangible benefits, even without achieving full “quantum advantage.” Second, it validates the hybrid model, where classical and quantum systems work in tandem rather than in competition.

Importantly, the trial demonstrated that quantum circuits can produce a diverse set of near-optimal solutions. This diversity is critical in logistics, where no single “perfect” solution exists, and flexibility is often more valuable than rigid optimization.

The Quantinuum Advantage

Quantinuum, formed from the merger of Honeywell Quantum Solutions and Cambridge Quantum, is one of the global leaders in trapped-ion quantum systems. Its H1 series processors are particularly suited to optimization tasks due to:

• High Fidelity: Quantum gates with >99.9% accuracy.

• All-to-All Connectivity: Unlike some architectures, any qubit can interact directly with any other, enabling efficient encoding of dense optimization problems.

• Mid-Circuit Measurement: Allowing iterative quantum workflows where circuits can adapt dynamically to intermediate results.

Quantinuum also invests heavily in quantum software. Its platforms, InQuanto and TKET, provided the tools for encoding logistics problems into quantum-ready form.

Aligning with BMW’s Digital Supply Chain Strategy

BMW’s long-term logistics vision extends far beyond quantum computing. By 2030, the company aims to have a fully digitized, predictive, and sustainable supply chain. Key elements of this transformation include:

• Predictive Routing: Leveraging AI and probabilistic models to forecast disruptions.

• Sustainability Analytics: Reducing emissions by minimizing unnecessary mileage and optimizing loads.

• Hybrid AI-Quantum Systems: Running co-optimization routines that balance cost, speed, and sustainability.

The quantum trial fits directly into this roadmap. Rather than waiting for fully mature quantum hardware, BMW is de-risking early adoption through pilots. By embedding these breakthroughs into its ERP environments powered by SAP and Siemens platforms, the company ensures that quantum innovation remains practical and integrable.

Industry and European Context

The BMW–Quantinuum collaboration also reflects broader trends across Europe. The EU’s Quantum Flagship program, funded at over €1 billion, explicitly supports logistics and supply chain research. Germany’s Quantum Computing Initiative, backed by more than €2 billion through 2026, positions the country as a leader in both hardware and applications.

Additionally, the Gaia-X digital supply chain project—a federated cloud and data-sharing infrastructure—is expected to provide secure pipelines for data feeding into quantum optimization workflows.

By demonstrating quantum applications in logistics, BMW becomes one of the first automakers to translate European quantum investments into practical industrial results.

From Pilot to Scaled Deployment

Following the May 2022 proof-of-concept, BMW and Quantinuum identified clear next steps:

1. Scaling Problem Size: Moving beyond 15 delivery points by leveraging Quantinuum’s upcoming H2 processor.

2. Software Integration: Embedding quantum solvers into BMW’s digital logistics control tower.

3. Multi-Objective Optimization: Balancing cost, emissions, and delivery times simultaneously.

4. Industry Collaboration: Establishing a quantum logistics working group under the European Automobile Manufacturers Association (ACEA).

These steps will enable gradual scaling from pilot studies to production-ready deployments.

Implications for Quantum Logistics Globally

BMW’s efforts join a global trend of automakers and logistics leaders experimenting with quantum technologies:

• Volkswagen partnered with D-Wave between 2019 and 2022 on traffic flow optimization in Beijing.

• Daimler collaborated with IBM Q to study quantum-enhanced routing for electric vehicle charging.

• Hyundai invested in IonQ for materials science and mobility research.

What distinguishes BMW’s trial is its use of trapped-ion hardware for live logistics simulations—a world first for the automotive sector. This not only demonstrates scalability potential but also highlights the near-term utility of hybrid classical-quantum workflows.

Conclusion

The May 12, 2022, proof-of-concept between BMW Group and Quantinuum illustrates how quantum computing is moving steadily from theory into practice within industrial logistics. By applying trapped-ion quantum processors to a real-world VRP case, the trial achieved measurable gains in efficiency and flexibility.

While the tested problem sizes remain small, the significance lies in the demonstration of hybrid quantum-classical workflows in an operational setting. For BMW, this is a step toward its 2030 vision of a fully digitized and sustainable supply chain. For Quantinuum, it validates trapped-ion technology as a competitive architecture for industry-grade optimization.

Most importantly, the collaboration signals that quantum logistics is no longer a speculative future, but a present-day research frontier where incremental advances can yield real business value. The path from pilot to scaled deployment remains challenging, but BMW’s proactive approach positions it at the forefront of a transformation that could reshape supply chain optimization worldwide.

The Port of Rotterdam, Europe’s largest and busiest seaport, has long been at the forefront of adopting advanced digital and operational technologies to keep pace with growing global trade demands. On April 26, 2022, the Port Authority announced a major milestone in its innovation journey—a collaborative pilot with Delft-based quantum control electronics company QBlox and Dutch applied research institute TNO. The project aimed to trial quantum-enabled optimization methods within critical port operations, including container stacking, berth allocation, and intermodal scheduling.

This initiative reflects a broader recognition that the computational intensity of modern logistics has exceeded what traditional optimization methods and even high-performance computing (HPC) can reliably deliver. As ports become increasingly congested, automated, and environmentally regulated, new technologies such as quantum computing—or quantum-inspired optimization—are being considered as potential enablers for efficiency and resilience.

Why Quantum at Europe’s Busiest Port?

The Port of Rotterdam handles over 14 million TEUs (twenty-foot equivalent units) annually, making it not just Europe’s largest port but one of the world’s most complex multimodal hubs. Beyond container traffic, it supports bulk carriers, inland shipping networks, railway hubs, road transport, and energy distribution systems.

Traditional HPC approaches already play a critical role in managing such complexity, particularly for modeling dynamic arrivals and departures, fuel usage, and equipment scheduling. However, several converging challenges—including increasing vessel sizes, the rise of autonomous equipment, the need for sustainable routing, and disruptions from both climate change and geopolitical shocks—have amplified the scale and complexity of optimization problems.

Quantum technologies, in theory, can outperform classical approaches in solving such combinatorial optimization tasks. While today’s quantum computers are still in their infancy, quantum-inspired or quantum-enabled simulations can already provide valuable insights by mimicking quantum mechanics’ ability to explore vast solution spaces more efficiently than classical heuristics.

The Partnership and Objectives

The April 2022 initiative brought together three key players:

• QBlox: A Delft-based startup specializing in scalable control stack solutions for superconducting and spin qubit systems. Although not building full processors, QBlox provides modular, cryo-compatible hardware essential to quantum research across Europe. For this pilot, QBlox contributed a simulation-ready control stack capable of reproducing quantum sampling behaviors within a classical emulation environment.

• TNO (Netherlands Organization for Applied Scientific Research): TNO’s logistics research group adapted its simulation engines—already used for Dutch smart logistics programs—to include pseudo-quantum solvers. These solvers emulated quantum-enhanced optimization behavior, enabling a side-by-side comparison with conventional metaheuristics.

• Port of Rotterdam Authority: The port set clear operational goals for the trial. These included reducing crane idle times, minimizing container reshuffling inefficiencies, improving berth planning for overlapping arrivals, and smoothing modal transfers at the rail-inland-sea interface.

Simulation and Emulation Setup

The April 2022 pilot did not use an actual quantum processor but instead relied on hybrid simulation. TNO modeled port operations using its established logistics simulation frameworks. QBlox’s pseudo-quantum control stack was then integrated to provide sampling behavior akin to that expected from future quantum processors.

The simulation targeted three operational use cases:

1. Container stacking sequences – optimizing crane movements under varying arrival conditions to reduce reshuffling.

2. Berth assignment planning – allocating docking slots dynamically based on ship class, tidal conditions, and expected throughput.

3. Intermodal transfer scheduling – coordinating transfers between inland waterway vessels, trucks, and rail terminals over three-day peak demand periods.

Performance was benchmarked against standard metaheuristic solvers to assess whether the quantum-inspired approaches provided measurable benefits.

Key Findings and Performance Metrics

The trial demonstrated early but meaningful improvements in simulated efficiency:

• 11% reduction in crane idle times under scenarios requiring high-density container stacking.

• 7% better berth utilization when ships arrived with overlapping schedules.

• 9% faster modal rescheduling during simulated disruption events, such as delayed arrivals or equipment downtime.

A critical insight was that the hybrid solver’s ability to escape local minima and present diverse solution sets contributed significantly to operational flexibility. While not yet replacing classical optimization entirely, the trial suggested that quantum-enabled methods could provide complementary tools in complex logistics decision-making.

Alignment with the Dutch National Quantum Strategy

The pilot aligns closely with the Netherlands’ broader Quantum Delta NL strategy, which aims to position the country as a leading quantum hub by 2027. The Delft–Rotterdam corridor is central to this ambition, hosting:

• TU Delft, a leader in superconducting qubit research.

• QBlox and QuantWare, hardware startups scaling quantum stack components.

• TNO, which bridges applied quantum science with industrial and national logistics programs.

For the Port of Rotterdam, the pilot was as much about future-proofing as about immediate gains. With rising competition from other global ports and increasing demands for digitally sovereign operations, testing quantum-enabled optimization underscores the port’s strategy to remain at the technological frontier.

Challenges and Outlook

The project team acknowledged several constraints in this early-stage trial:

• No true quantum processor was used; all results were derived from simulations.

• For small problem sizes, classical solvers still outperformed emulated quantum-inspired approaches.

• External port data—such as weather, customs processing, and labor availability—remains difficult to quantify in models, limiting the real-world applicability of results.

Nevertheless, the partners charted clear next steps:

• By 2024, integrating hybrid solvers with live telemetry from cranes and vessels.

• Testing compatibility with upcoming European quantum processors, including those available on Quantum Inspire.

• Scaling pilot scenarios to cover 50+ container bays and simulating week-long scheduling cycles.

Global Impact and Relevance

The Port of Rotterdam’s April 2022 pilot adds to a growing body of experiments worldwide. Notable initiatives include:

• Singapore’s PSA Group partnering with D-Wave on crane scheduling.

• Busan Port in South Korea evaluating quantum data pilots for logistics planning.

• Harwich International Port in the UK receiving funding for quantum transport security projects.

With its scale, strategic location, and strong ties to Europe’s quantum ecosystem, the Port of Rotterdam is uniquely positioned to push forward applied quantum logistics research.

Conclusion

The April 26, 2022, quantum-enabled optimization pilot at the Port of Rotterdam represents a significant step toward integrating next-generation computing into real-world logistics. While the trial relied on simulated environments rather than live quantum processors, the results showed clear performance improvements in critical operational metrics, such as crane efficiency, berth utilization, and intermodal scheduling.

By collaborating with Delft-based QBlox and TNO, the port reinforced its commitment to innovation while aligning with the Netherlands’ national quantum strategy. As quantum technology matures, Rotterdam’s early engagement could provide it with a first-mover advantage in transforming seaport operations—offering not only efficiency gains but also a pathway toward greener, more resilient, and digitally sovereign logistics systems.

At a time when global supply chains face unprecedented stress, initiatives like this underscore how the convergence of quantum science and logistics can deliver tangible solutions to complex challenges.

Maritime shipping is the backbone of global trade, accounting for more than 80% of goods transported worldwide. From raw materials to finished products, shipping lanes serve as the arteries of international commerce. Yet, the industry faces mounting challenges—fuel price volatility, port congestion, unpredictable weather, and climate-driven disruptions are straining the traditional logistics planning models that shipping operators have relied on for decades.

Against this backdrop, Fujitsu and MOL Logistics (MOL) announced on April 19, 2022, a landmark initiative to explore how advanced computing—specifically Fujitsu’s Digital Annealer—could transform the efficiency and resilience of maritime supply chains. Their collaboration underscores both Japan’s strategic embrace of next-generation technologies and the urgent need for innovation in the shipping industry.

A Quantum-Inspired Approach to Global Shipping Challenges

The quantum revolution has generated global excitement, but most practical quantum hardware remains in its infancy. Fujitsu’s Digital Annealer offers a bridge: a quantum-inspired computing architecture that mimics the logic of quantum annealing but operates on conventional hardware. The system is engineered to solve combinatorial optimization problems—complex puzzles where thousands of variables must be arranged for the best possible outcome.

In maritime logistics, optimization problems appear at every turn. Carriers must balance ship fuel consumption, port berth schedules, cargo mix, and container utilization, all while navigating environmental regulations and unpredictable disruptions. MOL Logistics, a key division of Mitsui O.S.K. Lines Group, manages an extensive portfolio of cargo solutions across sea, land, and air. For them, the potential of Fujitsu’s Digital Annealer lies in its ability to reimagine decision-making in Japan’s dense and strategically critical maritime corridors.

The Partnership in Detail

The April 2022 initiative brought together MOL’s logistics expertise and Fujitsu’s applied computing division to test how Digital Annealer-based models could improve operational efficiency. Specific pilot areas included:

• Port-to-port routing optimization across East Asia and Pacific trade hubs.

• Berth scheduling and slot allocation, enabling ships to avoid costly delays.

• Scenario planning for resilient routes under variable weather conditions and fuel cost fluctuations.

The trials leveraged Fujitsu’s computational models while drawing on MOL’s operational data to simulate realistic scenarios.

How Fujitsu’s Digital Annealer Works

The Digital Annealer is not a quantum computer in the strictest sense. It uses a CMOS (complementary metal-oxide-semiconductor) architecture to emulate aspects of quantum mechanics, particularly the concept of “tunneling” that allows exploration of complex solution spaces without getting trapped in local minima.

In practice, the system is adept at solving QUBO (Quadratic Unconstrained Binary Optimization) problems—mathematical formulations that capture the kinds of constraints and trade-offs inherent in logistics planning. For MOL Logistics, the Digital Annealer enabled rapid processing of thousands of possible routing combinations, delivering near-instant suggestions for fuel-efficient paths while factoring in port availability and container deadlines.

Real-World Use Cases Explored

The pilot simulations in April 2022 focused on intra-Asia container routes, including Yokohama to Busan, Manila, and Singapore. The tests included scenarios reflecting pandemic-era port congestion, weather-related slowdowns, and fluctuating shipping demand.

Key findings included:

• Fuel savings of up to 9% on selected trial routes.

• Improved scheduling accuracy by 12–15%, reducing delays at congested ports.

• Reduced empty-container repositioning, lowering operational costs and emissions.

These results highlighted the value of quantum-inspired computing in managing unpredictable variables in maritime logistics.

Industry and Regional Significance

The Fujitsu–MOL collaboration aligns with Japan’s broader policy objectives. The Ministry of Economy, Trade and Industry (METI) has placed supply chain resilience high on its agenda, while the Japanese government’s Moonshot R&D Program promotes quantum and advanced computing research for industrial applications. MOL’s digitalization roadmap also supports Japan’s carbon neutrality goals for 2050, making sustainability a key driver of this initiative.

Beyond Japan, the trial has regional implications. Maritime hubs in Singapore, Taiwan, and the Philippines face similar challenges and are also experimenting with advanced optimization technologies. By proving that a quantum-inspired tool can deliver tangible results today, Fujitsu and MOL set a precedent that other Asian ports are likely to follow.

Competitive Landscape in Quantum Maritime Logistics

This project does not exist in isolation. Around the world, other shipping and logistics players are piloting quantum or quantum-inspired tools:

• D-Wave and Port of Los Angeles tested container scheduling optimization.

• Classiq and ZIM Integrated Shipping Services explored quantum circuit design for freight routing.

• IBM Q and Maersk partnered on post-quantum cybersecurity for maritime systems.

What distinguishes Fujitsu’s Digital Annealer is its readiness for deployment. While true quantum systems remain limited by hardware constraints, the Digital Annealer offers immediate scalability and industrial reliability, positioning it as a commercially viable alternative.

Roadmap Toward Integration

Following the April pilot, Fujitsu and MOL Logistics laid out a roadmap for expansion:

1. Scaling beyond Asia to include global intermodal routes, such as trans-Pacific and Europe–Asia corridors.

2. Building an API layer for integration into MOL’s Transport Management Systems (TMS).

3. Developing a digital twin environment for container flow forecasting, combining quantum-inspired insights with real-time operational data.

The long-term vision is a hybrid logistics system where AI-driven prediction, real-time visibility, and quantum-inspired optimization converge into a unified decision-making platform.

Broader Implications for Logistics Technology

The Fujitsu–MOL collaboration illustrates a pragmatic path forward in logistics innovation. Instead of waiting for fully scalable quantum computers, the Digital Annealer enables near-term adoption of quantum-inspired methods, delivering measurable benefits today.

This has far-reaching implications:

• Emissions reduction through optimized routing and fewer unnecessary port calls.

• Scenario-based logistics planning, improving resilience to disruptions.

• Hardware-agnostic quantum adoption, ensuring companies can upgrade seamlessly as true quantum hardware matures.

Conclusion

The April 19, 2022 partnership between Fujitsu and MOL Logistics signals a pivotal moment for maritime logistics. By applying quantum-inspired computing to real-world shipping problems, the collaboration has demonstrated both immediate operational benefits and a blueprint for future digital transformation.

As global shipping continues to face unprecedented challenges, tools like the Digital Annealer offer a critical competitive edge. Fujitsu and MOL’s work shows that quantum-inspired logistics is not a distant vision but a practical solution already reshaping maritime efficiency today—one that could define the next era of sustainable global trade.

Addressing Port Congestion with Quantum-Inspired Technology

By early 2022, global supply chains were under significant strain. The COVID-19 pandemic, e-commerce growth, and shipping imbalances had created persistent congestion at major ports worldwide. Container dwell times increased, vessels were forced to wait offshore, and downstream industries experienced shortages. Japan, heavily reliant on efficient maritime logistics, faced these challenges acutely at its international gateways.

The Port of Yokohama, one of Japan’s busiest and most strategically important ports, turned to quantum-inspired technology for relief. On April 18, 2022, Fujitsu and port officials announced the successful deployment of Fujitsu’s Digital Annealer, a platform designed to solve large-scale combinatorial optimization problems. While not a quantum computer in the strictest sense, the Digital Annealer leverages principles from quantum annealing to deliver rapid optimization solutions, outperforming many classical systems in logistics settings.

By applying this technology to container allocation, berthing schedules, and yard management, the Port of Yokohama aimed to reduce inefficiencies that had long plagued maritime operations.

The Pilot Implementation

The pilot project was designed to address the most pressing pain points of port operations: ship berthing, container yard placement, truck scheduling, and routing inside the port. Specifically, the system focused on:

• Container berth scheduling: Determining which ship should dock at which berth, accounting for size, arrival time, and resource availability.

• Yard placement optimization: Reducing container reshuffling by strategically assigning slots in advance.

• Truck arrival forecasting: Using AI-powered models with weather and traffic inputs to predict arrival flows.

• Routing optimization within the port: Minimizing idle trailer time and congestion in yard lanes.

Fujitsu’s Digital Annealer modeled these interdependent tasks as Quadratic Unconstrained Binary Optimization (QUBO) problems. This mathematical framework allowed the system to run simulations on hundreds of container moves, producing optimized placement and retrieval sequences in near real-time.

The pilot was not just a proof of concept. It was run on live operational data, simulating real container volumes and vessel traffic, making it a genuine stress test of the platform’s utility in day-to-day logistics.

Results and Measurable Impact

The outcomes of the pilot were tangible and quantifiable:

• Container dwell time was reduced by an average of 12%, meaning containers spent less time waiting in the yard before pickup.

• Gate-in/gate-out speeds for trucks improved by 18%, allowing faster cargo retrieval and turnaround.

• Yard crane movements became 9% more efficient, reducing unnecessary repositioning.

These improvements translated into faster ship turnarounds, reduced congestion, and smoother operations for port staff and logistics companies.

Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT), which backed the initiative, praised the results, stating that the pilot provided “a blueprint for digitized, resilient port operations” that could scale across other Japanese ports.

Technical Architecture and Integration

The system architecture reflected a blend of modern optimization with legacy operations:

• Data ingestion layer: Integrated berth schedules, manifests, truck appointment data, and yard maps.

• Pre-processing engine: Converted operational data into QUBO models suitable for the Digital Annealer.

• Digital Annealer backend: Solved optimization problems within sub-seconds.

• Visualization dashboard: Gave operators side-by-side comparisons of baseline operations and optimized scenarios.

A key achievement was that Fujitsu’s solution worked alongside legacy terminal operating systems (TOS). Rather than replacing existing infrastructure, it operated in hybrid mode, ensuring smoother adoption.

Broader Relevance for Maritime Logistics

The significance of the Yokohama pilot extends far beyond Japan. Ports worldwide — from Rotterdam to Singapore — grapple with the same fundamental issues: container backlogs, unpredictable ship arrivals, and truck bottlenecks.

By demonstrating that quantum-inspired solutions can generate measurable efficiency gains today, Fujitsu positioned itself at the forefront of maritime digital transformation. Unlike pure research projects on quantum hardware, this was an operational deployment with measurable business impact, making it highly replicable across global logistics hubs.

Policy Support and National Strategy

The pilot was not a standalone initiative. It was backed by the Japanese government under its Green Innovation Fund and Digital Garden City Nation initiative. These national programs aim to digitize critical infrastructure while also advancing sustainability goals.

Reducing container congestion has environmental benefits as well. During the pilot, reduced idling for diesel trucks and yard equipment cut CO₂ emissions by an estimated 4.7%. This aligns with Japan’s broader climate strategy and reinforces the link between operational efficiency and sustainability.

Industry Reactions and Roadmap

Industry observers welcomed the results. The Nittsu Research Institute, part of Nippon Express Holdings, noted that quantum-inspired routing could become “essential for intermodal synchronization.” Meanwhile, shipping lines NYK Line and MOL Logistics expressed interest in similar pilots for other Japanese ports.

Fujitsu’s roadmap following the pilot includes:

• Scaling the solution to multiple terminals and berths.

• Integrating customs and inspection scheduling data to further streamline workflows.

• Introducing dynamic reoptimization, accounting for live vessel delays and weather disruptions.

• Exporting the solution to Southeast Asia, the Middle East, and Europe.

This global ambition underlines Fujitsu’s intent to make its Digital Annealer a key logistics optimization tool beyond Japan.

Quantum-Inspired vs. True Quantum

It is important to distinguish between quantum-inspired systems like the Digital Annealer and true quantum hardware.

The Digital Annealer does not rely on quantum superposition or entanglement. Instead, it uses specialized CMOS circuits that mimic the mathematical properties of quantum annealing, enabling it to efficiently solve QUBO problems at scale.

While it lacks the theoretical power of universal quantum computers, its strength lies in practicality. True quantum systems remain fragile, requiring cryogenic environments and high error correction overheads. By contrast, the Digital Annealer is deployable today, providing industries with a bridge toward future quantum adoption.

Conclusion

The April 18, 2022, announcement by Fujitsu and the Port of Yokohama marks a significant step forward in applying advanced optimization technologies to maritime logistics. By cutting container dwell times, accelerating truck throughput, and reducing yard congestion, the pilot demonstrated clear operational and environmental benefits.

For Japan, it reinforces national priorities in digital infrastructure and sustainability. For the global shipping industry, it provides a replicable model of how quantum-inspired optimization can unlock near-term value while preparing the ground for future quantum hardware.

As congestion continues to challenge global supply chains, Fujitsu’s Digital Annealer offers a glimpse into how quantum-inspired tools can reshape logistics efficiency today, while paving the way toward the full realization of quantum computing in tomorrow’s supply chains.

Europe’s Busiest Port Meets Quantum Innovation

The Port of Rotterdam, often referred to as the “gateway to Europe,” is the continent’s largest and busiest seaport, handling more than 14 million TEUs (twenty-foot equivalent units) annually. With thousands of ships, trains, trucks, and barges moving through the port each day, its operations demand constant synchronization to prevent bottlenecks. Efficient handling of containers is vital to minimize turnaround times, reduce environmental impacts, and maintain Rotterdam’s role as a global logistics hub.

On April 19, 2022, the Port Authority announced a new initiative in partnership with Delft University of Technology (TU Delft), specifically its world-renowned quantum research center QuTech. The collaboration marks one of the first attempts to test quantum optimization algorithms in a real-world logistics environment. The effort is embedded within the Dutch national program Quantum Delta NL, a €615 million initiative aimed at establishing the Netherlands as a global leader in quantum innovation.

This pilot is not a mere academic exercise. Instead, it seeks to apply quantum computing directly to operational challenges, particularly container scheduling, crane allocation, and intermodal logistics. If successful, it could redefine how ports worldwide handle ever-increasing cargo flows.

Inside the Pilot: Quantum Optimization in Practice

The initial pilot focused on container transshipment scheduling, an area notoriously complex due to the interdependence of ship arrivals, crane assignments, and onward connections via road, rail, and inland waterway. The problem becomes exponentially more difficult to solve when factoring in real-world conditions such as weather delays, sudden surges in vessel arrivals, and customs requirements.

The technical framework included:

• Hybrid Quantum-Classical Solvers: TU Delft researchers used D-Wave’s hybrid solver environment, which allows classical computers to work alongside quantum processors for faster optimization.

• QUBO Modeling: Operational challenges were translated into Quadratic Unconstrained Binary Optimization (QUBO) models, a mathematical framework suited for quantum computation.

• Port Data Integration: Real-world data from the Rotterdam Port Community System, which aggregates information from terminal operators, shipping lines, and logistics partners, fed directly into the simulations.

• Benchmarking: Performance was compared against classical heuristic models currently used by port authorities.

The simulations covered scenarios ranging from crane allocation under variable workloads to scheduling disruptions caused by tidal conditions or traffic congestion.

Early Results and Observations

Though still in simulation, the pilot produced results that strongly suggest quantum optimization can outperform existing methods:

• Crane Utilization: Efficiency improved by 7–10%, enabling more containers to be handled per hour with the same resources.

• Reduced Waiting Times: Inbound container dwell times fell by as much as 12%, improving vessel turnaround.

• Environmental Gains: Better scheduling reduced truck idling and barge delays, lowering CO₂ emissions across the logistics chain.

• Resilience: Quantum-enhanced models recomputed optimal schedules faster when disruptions occurred, offering a clear advantage in dynamic operational environments.

Rotterdam officials emphasized that while these outcomes remain within controlled digital twin simulations, they represent a tangible pathway toward live implementation.

A Strategic Fit with Quantum Delta NL

The Rotterdam pilot forms part of Quantum Delta NL, the Netherlands’ flagship quantum initiative. With substantial funding and a strong academic-industry partnership structure, the program seeks to push quantum technology beyond labs into critical sectors such as healthcare, cybersecurity, and logistics.

Supporting partners in this ecosystem include:

• QuTech (TU Delft + TNO): Leading research into quantum hardware and algorithms.

• Portbase: Providing the national data-sharing backbone for logistics inputs.

• Eurofiber & SURF: Delivering high-performance network and cloud infrastructure, essential for hybrid quantum-classical workflows.

By embedding port optimization into this national strategy, Rotterdam demonstrates how Europe can integrate advanced digital technologies into its most vital infrastructure.

Quantum Logistics: A New Frontier for Port Operations

Port logistics represent one of the most complex optimization challenges globally. Tasks such as crane scheduling, berth allocation, customs clearance, and multimodal traffic coordination involve thousands of interdependent variables. While classical computing handles many scenarios effectively, the scale and unpredictability of global trade make some optimization problems nearly intractable.

Quantum computing offers an alternative by leveraging principles of superposition and entanglement to explore multiple possible solutions simultaneously. Even today’s hybrid solvers—where quantum and classical processors share workloads—can deliver advantages in time-sensitive logistics.

In Rotterdam, quantum optimization could allow port operators to predict and respond to disruptions more quickly, synchronize container movements across terminals, and reduce energy consumption in the process.

Aligning with Rotterdam’s Digital Twin Strategy

For years, the Port of Rotterdam has invested in developing a full-scale digital twin—a virtual replica of its physical operations that updates in real time. This digital twin provides a safe, non-disruptive environment to test advanced algorithms before live deployment.

Integrating quantum pilots into the digital twin strategy offers several advantages:

• Scenario Testing: Quantum algorithms can model extreme “what if” cases such as sudden surges in vessel arrivals.

• Predictive Accuracy: Enhanced optimization improves forecasting of cargo flows.

• Environmental Goals: Better coordination reduces unnecessary movements, supporting Rotterdam’s ambition to be carbon-neutral by 2050.

The digital twin thus serves as both a testbed and a stepping stone toward future live integration of quantum computing.

European and Global Relevance

Rotterdam’s pilot has implications far beyond the Netherlands. European ports are under increasing pressure to modernize due to global supply chain shocks, stricter emissions targets, and competition from Asia-Pacific hubs like Singapore and Shanghai.

The European Union’s Horizon Europe program actively supports quantum-driven logistics projects, underscoring their importance to the continent’s digital transformation. Meanwhile, other ports are exploring similar initiatives:

• Hamburg: Testing AI and quantum methods for berth allocation.

• Antwerp: Exploring quantum cryptography for secure data flows.

• Singapore: Partnering with IBM to apply quantum to logistics forecasting.

Rotterdam’s April 2022 initiative sets a precedent by demonstrating how quantum science can be applied not just in labs but in the heart of industrial-scale logistics.

Next Steps and Roadmap

The Port Authority and TU Delft outlined several next steps following the pilot:

1. Expand Scope: Scale up from single-terminal simulations to multi-terminal and intermodal operations.

2. Test Advanced Algorithms: Apply cutting-edge quantum processors from European research programs.

3. Live Trials: Launch on-site pilot deployments by early 2023.

4. Open Collaboration: Feed improvements into open-source logistics frameworks such as OpenTCS and CargoSmart.

These steps aim to ensure that quantum optimization transitions from concept to practice in the near future.

Quantum and Logistics: Industry Implications

The Rotterdam pilot is part of a growing global movement to explore quantum’s role in supply chains:

• D-Wave & SavantX: Optimized crane scheduling in California ports.

• IonQ & Hyundai: Applied quantum AI to mobility and logistics.

• IBM & Maersk: Collaborated on quantum risk modeling for supply chains.

Together, these efforts highlight an emerging consensus: logistics and supply chain optimization will likely be among the first real-world domains where quantum advantage becomes visible.

Conclusion

The April 19, 2022 quantum optimization pilot at the Port of Rotterdam represents a pivotal step toward integrating cutting-edge quantum technologies into the real-world fabric of global logistics. By working with TU Delft under the Quantum Delta NL initiative, the port is demonstrating how hybrid quantum-classical solutions can improve container scheduling, crane utilization, and intermodal efficiency.

While still in simulation, the results show measurable promise—reduced waiting times, better crane throughput, and lower emissions. More importantly, they position Rotterdam at the forefront of digital transformation, leveraging quantum computing not as a future dream but as a present-day tool.

As ports worldwide face mounting challenges from supply chain disruptions to climate pressures, Rotterdam’s pioneering effort may serve as a global model for how quantum technology can reshape the flow of goods that sustain modern economies.

Toward Mobile Quantum Optimization: An Industry First

Quantum computing has long been associated with specialized laboratories and large, cryogenically cooled hardware. Yet, in March 2022, a groundbreaking partnership challenged this paradigm. Quantum Brilliance, an Australian-German quantum hardware startup, teamed with Canadian National Railway (CN) to investigate how portable quantum devices could enhance rail freight operations. The collaboration is designed to test compact, diamond-based accelerators capable of operating at room temperature—bringing quantum computing directly into the cab of locomotives and across CN’s extensive North American freight network.

This effort represents one of the earliest attempts to test quantum processors in rugged, mobile environments. Unlike previous research that relied heavily on cloud-based access to centralized quantum machines, this project is focused on “quantum at the edge,” where decisions must be made instantly and in direct proximity to moving assets.

Quantum Brilliance: A Different Hardware Path

Quantum Brilliance’s technology is distinct within the crowded field of quantum hardware. Instead of relying on superconducting circuits or trapped ions that require cryogenic cooling and complex infrastructure, the company’s systems are built using nitrogen-vacancy (NV) centers in synthetic diamonds.

This architecture offers three major advantages:

1. Room-temperature operation – eliminating the need for dilution refrigerators.

2. Compact form factor – small enough to be housed in data centers, warehouses, or even vehicles.

3. Energy efficiency – significantly lower consumption compared to cryogenic quantum machines.

While today’s diamond-based accelerators are relatively small in scale, typically managing between two and five logical qubits, they are particularly well-suited for optimization problems. In hybrid quantum-classical workflows, even small accelerators can assist in solving combinatorial bottlenecks that slow traditional systems. For CN, the value lies in deploying these devices directly at the edge of operations, where they can improve scheduling and routing in real time.

The CN Rail Network: A Complex Testbed

Canadian National Railway operates one of the largest and most integrated rail systems in North America. Its 20,000-mile network stretches across Canada and deep into the United States, with transcontinental routes linking Halifax to Vancouver and lines extending into the Midwest and Gulf Coast.

CN’s operations are tightly interwoven with ports, intermodal terminals, and trucking networks, creating immense logistical complexity. Key operational challenges include:

• Prioritizing cargo across trains under strict delivery windows.

• Managing rail congestion during peak shipping cycles.

• Optimizing fuel usage on long-haul routes.

• Scheduling rolling stock maintenance with minimal downtime.

These challenges involve thousands of simultaneous variables that must be computed in near real time. CN has already invested in artificial intelligence, IoT-enabled sensors, and predictive analytics. The March 2022 partnership with Quantum Brilliance marks its first step into quantum-enabled logistics.

Research Scope: Three Pilot Use Cases

The joint initiative identified three initial use cases for field testing quantum devices onboard locomotives and at rail yards.

1. Train Consist Optimization
   Quantum solvers could dynamically determine optimal car groupings based on destination, weight, speed, and priority. When freight cars are added or removed mid-route, the onboard quantum device could adjust the consist plan instantly.

2. Fuel Routing Optimization
   Trains consume massive amounts of fuel, making route optimization critical. Quantum Brilliance’s accelerators will be tested for their ability to calculate real-time switch points and acceleration curves that minimize fuel burn, incorporating live telemetry from locomotives.

3. Maintenance Window Scheduling
   Freight reliability depends on predictive maintenance. Using sensor data, onboard quantum devices could triage equipment issues and generate optimized repair schedules, reducing unplanned downtime.

Each of these use cases emphasizes low-latency decision-making, where the ability to compute locally provides a strategic advantage over cloud-based solutions.

Advantages of Portable Quantum Accelerators

The CN–Quantum Brilliance project aims to validate several potential benefits of edge-deployed quantum devices:

• Reduced latency – decisions can be made onboard without transmitting data to central servers.

• Low energy demand – enabling deployment in mobile environments like locomotives.

• Hybrid orchestration – functioning as co-processors to existing AI and optimization systems.

Even small-scale devices may provide measurable improvements in optimization cycles, particularly when combined with classical solvers.

Supporting Infrastructure and Research Collaboration

The research project aligns with CN’s broader digital transformation efforts. The railway is actively rolling out IoT sensors across rolling stock, expanding its use of predictive AI for track and railcar health, and building out 5G-enabled edge computing.

Quantum Brilliance, meanwhile, has secured international support from the National Research Council of Canada and the Australian Trade and Investment Commission. This underscores the cross-continental nature of the project and its alignment with Canada’s National Quantum Strategy, released in 2022, which specifically encourages logistics and transportation quantum pilots.

Challenges and Open Questions

While the announcement sparked excitement, several limitations remain:

• Low qubit counts limit the scale of problems that can be solved today.

• Noise and environmental conditions could affect performance in mobile settings.

• Integration with CN’s legacy systems requires middleware development and operator training.

Nevertheless, both partners emphasized that the project’s value lies in testing practicality, not theoretical breakthroughs. By conducting real-world trials, they aim to uncover where portable quantum devices can already add measurable value.

Global Implications: Quantum at the Edge

The collaboration signals a new chapter for logistics quantumization. Until now, most quantum projects have been confined to research labs or accessed through the cloud. Bringing devices directly into operational environments—on trains, in warehouses, or at intermodal yards—marks a significant shift.

Globally, the rail sector is watching closely. Freight operators in Europe and Asia are exploring quantum optimization for scheduling, but few have considered deploying hardware onboard trains themselves. If successful, CN’s trial could serve as a blueprint for railroads worldwide.

Next Steps and Expansion

Following the March announcement, CN and Quantum Brilliance outlined their next steps:

• Installing prototype quantum accelerators in select locomotive cabs.

• Running hybrid optimization workloads in parallel with classical systems.

• Expanding scope to include customs clearance and intermodal scheduling in collaboration with border authorities.

Quantum Brilliance is also pursuing additional partnerships across Australia, Germany, and the United States, with interest from postal logistics, short-haul rail, and warehouse automation providers.

Conclusion: Toward a New Era of Embedded Quantum Logistics

The March 28, 2022 announcement of the CN–Quantum Brilliance partnership highlights an inflection point in the quantum computing journey. Rather than waiting for large-scale, fault-tolerant quantum computers, industry leaders are beginning to explore where smaller, specialized devices can deliver practical benefits today.

By embedding quantum accelerators directly into locomotives and maintenance facilities, CN and Quantum Brilliance are testing whether optimization can be made faster, more adaptive, and more resilient at the operational edge. If successful, this initiative could demonstrate that the future of quantum computing lies not only in centralized data centers but also in compact, embedded systems shaping logistics decisions in real time.

As global freight networks face mounting pressure to reduce costs, improve efficiency, and meet environmental goals, portable quantum devices could offer a critical competitive edge—marking the beginning of a new era in rail and logistics innovation.

Japan has long been recognized as a global leader in shipping, industrial modernization, and efficient logistics systems. On March 22, 2022, the nation made another step forward by embracing quantum-inspired computing to address one of its most pressing infrastructure challenges: coordinating intermodal transport across seaports, rail networks, and trucking carriers.

The Ministry of Land, Infrastructure, Transport and Tourism (MLIT), in collaboration with the ports of Yokohama and Kobe, partnered with Fujitsu Limited to launch a pilot program designed to optimize scheduling through Fujitsu’s Digital Annealer platform. While not a full quantum computer, the Digital Annealer leverages quantum-inspired techniques to solve complex combinatorial optimization problems, making it well-suited for large-scale logistics applications.

Quantum Computing Meets Japanese Port Modernization

Ports are the beating heart of Japan’s import-export economy. With over 90% of trade volume moving through maritime gateways, efficient coordination between sea, rail, and road transport is essential. Yet legacy scheduling systems—many built decades ago—are increasingly strained by surging trade flows, pandemic-related disruptions, and growing pressure to decarbonize.

The March 2022 pilot represented Japan’s first significant attempt to apply quantum-inspired optimization directly to port operations. By deploying Fujitsu’s Digital Annealer, authorities sought to dynamically align berth allocation, container slotting, and handoffs between trucks and rail operators. Unlike static scheduling systems, the Digital Annealer could respond in real time to disruptions ranging from weather delays to labor shortages, recalculating optimal schedules in seconds.

The Logistics Challenge: Bottlenecks in Modal Coordination

Japan’s intermodal hubs face several operational bottlenecks:

• Misaligned container unloading and rail departures: Ships often unload cargo hours before connecting trains are ready, causing costly storage delays.

• Idle truck waiting times: Drivers frequently wait long hours at terminals for containers that are not yet positioned.

• Underutilized yard equipment: Inefficient slotting reduces throughput and increases operational costs.

Traditional scheduling software struggles with the sheer scale of these problems. Variables like ship arrival times, labor availability, weather conditions, and equipment capacity interact in unpredictable ways, creating a high-dimensional optimization challenge.

Quantum-inspired systems, such as Fujitsu’s Digital Annealer, are uniquely positioned to handle such complexity.

The Fujitsu Digital Annealer Advantage

Fujitsu’s Digital Annealer simulates the mathematical process of quantum annealing using classical hardware. This allows it to rapidly evaluate massive solution spaces and identify optimal or near-optimal answers to complex problems.

For the port pilot, the Digital Annealer was tasked with:

• Dynamic berth allocation: Matching ship arrivals with optimal docking slots.

• Container slot scheduling: Assigning containers to yard positions that minimize retrieval times.

• Truck-rail-maritime synchronization: Ensuring handoff windows between modes are precisely aligned.

The platform had previously been applied in Japanese finance and supply chain projects, but this pilot marked its first use in maritime logistics.

Pilot Structure: Yokohama and Kobe Lead the Charge

The ports of Yokohama and Kobe were chosen for the pilot due to their heavy cargo volumes and established intermodal infrastructure.

• Port of Yokohama: One of Japan’s largest ports, handling over 35 million tons of cargo annually, with a strong emphasis on container traffic.

• Port of Kobe: A historic trade hub with a strategic position in western Japan’s logistics network.

Both ports collaborated with JR Freight and trucking operators to feed real-time data into Fujitsu’s system. The Digital Annealer processed these inputs to generate scheduling recommendations, which were compared against traditional planning methods.

Early Results: Reduced Idle Times and Bottlenecks

The March 2022 trials ran in parallel with existing systems, ensuring no operational risk. The results were promising:

• 11% reduction in average truck idle time at terminals.

• 15% improvement in berth utilization, increasing overall throughput.

• More reliable intermodal handoffs, particularly during weather disruptions.

Stakeholders noted that the ability to continuously adapt schedules in real time marked a significant improvement over static, rule-based systems.

Japan’s National Quantum Strategy and Port Digitization

The pilot aligned closely with Japan’s broader technology roadmaps.

• Quantum Technology Innovation Strategy (2020): Japan’s national framework for advancing quantum applications in transport, finance, and manufacturing.

• Smart Port Initiative (MLIT): A mandate to digitize all major Japanese ports by 2025, integrating AI, IoT, 5G, and quantum-inspired optimization.

• Society 5.0 Vision: Japan’s national effort to integrate cyber-physical systems, where quantum technologies play a role in real-time optimization.

By embedding quantum-inspired tools into its port modernization agenda, Japan signaled its intent to compete globally on both efficiency and innovation.

Global Context: Smart Ports and Quantumization

Japan is not alone in its quantum logistics experiments.

• PSA Singapore partnered with D-Wave in 2022 to test crane scheduling optimization.

• Port of Hamburg collaborated with Airbus to study quantum-enhanced air-sea scheduling.

• Port of Los Angeles began developing digital twins incorporating quantum algorithms.

The Japanese pilot adds momentum to this global movement, showing that practical, near-term quantum-inspired applications can be deployed before fully fault-tolerant quantum computers arrive.

Strategic Implications and Roadmap

Following the pilot, MLIT announced plans to expand testing to the ports of Nagoya and Osaka Bay, while Fujitsu signaled its intent to extend the solution to railway cargo operators, trucking dispatch systems, and warehouse scheduling.

The long-term vision is end-to-end logistics synchronization, from vessel arrival to last-mile delivery. MLIT also linked the initiative to Japan’s green corridor programs, positioning quantum-inspired optimization as a tool for reducing emissions through better resource use.

Challenges and Human Factors

Despite the encouraging results, challenges remain:

• Legacy IT systems at ports must be upgraded to integrate seamlessly with optimization platforms.

• Human trust and adoption: Dispatchers and schedulers must be trained to understand and rely on AI- and quantum-aided decisions.

• Data completeness: Ensuring consistent real-time feeds from weather, traffic, and equipment remains a technical hurdle.

Fujitsu is addressing these issues through explainable AI interfaces and joint training programs with port authorities.

Conclusion

Japan’s March 22, 2022 pilot represents a significant milestone in applying quantum-inspired optimization to real-world logistics. By reducing idle times, improving berth utilization, and enabling dynamic intermodal coordination, the Fujitsu-powered project demonstrates that quantum-inspired computing is more than a theoretical promise—it is a practical tool for today’s infrastructure challenges.

As MLIT and Fujitsu expand the rollout to more ports and logistics nodes, Japan is positioning itself at the forefront of smart port innovation. The initiative not only supports economic efficiency and supply chain resilience but also contributes to environmental goals by minimizing wasted fuel and emissions.

In the broader context of global logistics, the Japanese pilot signals a new era: quantum-enhanced optimization is emerging as a competitive edge. Nations and companies that adopt these technologies early may define the future of freight mobility well before fully realized quantum computers arrive.

DHL’s Quantum Ambitions: Beyond Pilots to Real Operations

 Deutsche Post DHL Group, the world’s largest logistics provider, has built its reputation on being an early adopter of transformative technologies—from robotics in warehouses to blockchain for shipment verification. On March 15, 2022, DHL took a decisive step into the next frontier: quantum computing. The company announced a strategic collaboration with Terra Quantum AG, a Swiss-German firm specializing in hybrid quantum software, to explore how quantum optimization can strengthen Europe’s freight routing and warehousing networks.

The partnership goes beyond exploratory research. DHL emphasized its intent to move past proofs of concept and into operational use cases where quantum-inspired optimization can deliver tangible benefits. These include dynamic rerouting of freight trucks across congested urban corridors, slotting optimization for inbound containers at hubs, and minimizing idle time at cross-docking facilities. In an industry where minutes matter, the ability to improve these metrics even modestly can translate into millions in cost savings and measurable environmental impact.

Inside the DHL–Terra Quantum Research Program
At the heart of the collaboration lies Terra Quantum’s expertise in quantum-inspired optimization. Unlike quantum gate-model hardware, which is still in development, quantum-inspired algorithms run efficiently on classical infrastructure while incorporating principles of quantum mechanics, such as tunneling and entanglement. This makes them deployable today at scale.

For DHL, Terra Quantum developed a customized solution framework composed of three key modules:

1. Quantum-inspired routing solvers capable of adapting delivery routes in real time under constraints such as congestion, accidents, or delivery time windows.

2. Hybrid warehouse slotting algorithms designed to improve container handling and throughput within logistics hubs.

3. Simulation modules for quantum error mitigation, ensuring that algorithms remain robust under real-world uncertainty.

To test these solutions, DHL deployed simulations across critical freight corridors in Europe. Trials focused on the Rhine-Ruhr region in Germany, the Po Valley distribution zones in northern Italy, and cross-border nodes connecting Austria and Hungary. These locations were selected due to their high freight volumes and strategic importance to pan-European logistics flows.

Early Results Show Promising Gains

Initial findings from the March 2022 pilot were encouraging. DHL reported up to 12% faster rerouting capabilities during peak traffic disruptions, a 9% reduction in idle truck time across key terminals, and a 7% increase in adherence to tight delivery windows.

While the percentages may appear modest, within DHL’s vast network such improvements carry enormous weight. For example, reducing idle time by even a few minutes per truck can save significant fuel costs and reduce emissions across thousands of journeys daily. DHL and Terra Quantum codified their methodology and results into a jointly published white paper, contributing insights to the broader research community on quantum logistics applications.

Strategic Goals and Future Roadmap

DHL framed the collaboration with Terra Quantum as part of its broader roadmap to digitize and decarbonize logistics operations. Strategic objectives include cutting emissions through better load and route optimization, enhancing supply chain resilience under disruption, and building in-house expertise in quantum-ready tooling.

By the end of 2022, DHL aimed to expand the program’s scope to include:

• Ocean freight optimization and intermodal hubs.

• AI-quantum co-optimization initiatives with DHL’s analytics teams.

• Integration into DHL’s SmartTruck and Freight Visibility platforms, which form the backbone of its European road freight operations.

The Terra Quantum Advantage

Founded in 2019, Terra Quantum operates from St. Gallen, Switzerland, and Munich, Germany. The company has positioned itself as a European alternative to American quantum startups, with strong compliance to EU data privacy and digital sovereignty standards. Its flagship offerings include Quantum-as-a-Service (QaaS) and QIOS, a quantum-inspired optimization suite designed for industries including logistics, finance, and energy.

Partnerships with institutions such as ETH Zurich and Fraunhofer Institutes have bolstered Terra Quantum’s credibility. Its focus on applied hybrid quantum solutions made it a natural fit for DHL, which sought operational tools rather than theoretical prototypes.

Quantum Optimization: A Growing Freight Sector Trend

DHL’s venture into quantum optimization reflects a broader shift across the freight sector. Other logistics leaders have also begun exploring quantum technologies. In early 2022, Maersk launched a study with Rigetti to apply quantum approaches to port scheduling. DB Schenker partnered with IBM to explore post-quantum cryptography for supply chain resilience. Meanwhile, Kuehne+Nagel piloted route modeling using Oxford Quantum Circuits’ quantum emulators.

Together, these moves signal an industry-wide race to identify and scale quantum advantage in route efficiency, emissions reduction, and network resilience.

Regulatory and Policy Alignment

The DHL–Terra Quantum collaboration also aligns closely with European policy. Germany’s National Quantum Strategy, announced in 2022, allocated €3 billion toward quantum research and applications. At the EU level, the Quantum Flagship initiative funds pilots across multiple industries, including logistics. Additionally, Bavaria’s Quantum Valley program has actively supported deployment of quantum technologies in industrial sectors, including DHL’s southern German hubs.

This alignment with policy ensures that the DHL–Terra Quantum project benefits from both funding opportunities and regulatory support, making the path to deployment smoother.

Challenges and Technical Considerations

Despite its promise, the program revealed some challenges. Quantum-inspired solvers require careful scaling to handle the massive size of DHL’s freight operations. Data ingestion speed proved critical; slow integration of traffic and hub data limited the responsiveness of optimization algorithms in some tests. Another challenge is talent: DHL and Terra Quantum both acknowledged the difficulty of finding logistics engineers trained in quantum computing principles.

To overcome these barriers, DHL established a cross-functional team of logistics specialists, data scientists, and quantum researchers to co-develop domain-specific models.

From Simulation to Deployment: The Next Phase

With simulation trials complete, DHL outlined plans to move into live deployment phases. Early rollouts targeted Frankfurt am Main, with SmartTruck integrations for real-time traffic optimization, and Linz, Austria, for cross-border freight management. DHL Express Europe hubs were also slated for pilot programs using AI-quantum coordination to improve performance in time-definite delivery services.

Another focus was quantum-enhanced network design: evaluating how to reconfigure hub locations and fleet deployments for maximum efficiency using combinatorial simulations. This approach could shape the long-term structure of DHL’s European freight network.

Implications for the Global Logistics Sector

The DHL–Terra Quantum partnership highlights a key inflection point: hybrid quantum optimization is evolving from a research curiosity into an enterprise-grade tool. By applying it to freight routing, warehouse slotting, and hub management, DHL is demonstrating how quantum technologies can solve concrete, large-scale industrial problems.

For the global logistics sector, this move reinforces the growing belief that quantum optimization could become a standard tool for real-time freight decision-making and multi-objective planning—balancing cost, emissions, and service levels simultaneously.

Conclusion

The March 15, 2022 announcement of DHL’s collaboration with Terra Quantum marks one of the most important early steps in the industrial application of quantum computing. Moving beyond pilots, DHL is actively testing hybrid quantum solutions in real freight environments across Europe. Though technical and organizational challenges remain, the potential rewards—improved efficiency, reduced emissions, and greater supply chain resilience—are significant. As one of the world’s largest logistics providers, DHL’s embrace of quantum technology signals that the logistics industry is entering a new era of computational innovation, where quantum optimization could soon become a core component of global freight operations.

Quantum Logistics Crosses the Atlantic

On March 9, 2022, a pivotal milestone in global freight optimization unfolded when Canadian quantum computing pioneer D-Wave Systems and U.S.-based AI logistics firm SavantX announced the expansion of their Hyper Optimization Nodal Efficiency (HONE) platform into Europe. Following the successful deployment of their hybrid quantum-classical solution at the Port of Los Angeles in 2021, the two companies revealed new pilot projects at Hamburg, Germany, and Rotterdam, Netherlands—two of Europe’s largest and busiest container hubs.

This expansion was not just another port technology trial. It signaled a transatlantic leap for quantum logistics, representing one of the earliest real-world European deployments of quantum annealing systems applied directly to container scheduling, crane management, and real-time traffic flow in freight operations.

Quantum Meets Logistics Complexity

The complexity of port logistics lies in its dense web of interdependent tasks. Every day, tens of thousands of containers arrive, depart, and shift locations across terminal yards. Each movement must be orchestrated: where to place a container, which crane to assign, when to dispatch an automated guided vehicle (AGV), and how to prioritize certain high-value or time-sensitive cargo.

Traditional computational methods rely on heuristic algorithms and linear optimizers. While adequate for structured scenarios, these systems struggle under volatile real-world conditions like congestion spikes, equipment breakdowns, or the need to reroute containers for customs or inspection. Suboptimal decisions compound quickly, leading to wasted crane capacity, increased truck wait times, and elevated emissions.

Quantum annealing provides a new approach. By encoding these scheduling problems as Quadratic Unconstrained Binary Optimization (QUBO) models, D-Wave’s quantum system can explore billions of possible assignment configurations simultaneously, delivering schedules that minimize conflicts and maximize throughput.

The HONE platform integrates this capability through a four-layer architecture:

1. Quantum Annealing – Using D-Wave’s Advantage system to solve QUBO-based optimization challenges.

2. AI Pre-Processing – SavantX’s machine learning algorithms translate operational data into QUBO-ready models.

3. Real-Time Feedback Looping – Solutions adapt dynamically as cargo arrivals, equipment availability, and conditions shift.

4. Hybrid Cloud Integration – Results stream directly into terminal management systems (TMS) through secure APIs, ensuring compatibility with existing software ecosystems.

For the European pilots, this architecture was tailored to integrate with German and Dutch port software standards, ensuring compliance with EU data security regulations and cybersecurity frameworks.

Hamburg and Rotterdam: Strategic Pilot Sites

The choice of Hamburg and Rotterdam was deliberate. Together, the two ports process more than 20 million TEU (twenty-foot equivalent units) annually, serving as critical gateways for Europe’s global trade.

The March 2022 deployment targeted:

• Hamburg’s CTA terminal – focusing on container crane scheduling.

• Rotterdam’s Maasvlakte 2 terminal – applying quantum optimization to container stacking and retrieval logistics.

• AGV Routing – testing improved real-time routing algorithms for automated vehicles.

Early Results from the four-week trial showed measurable improvements:

• 12% reduction in crane idle time.

• 8% increase in container throughput.

• 6% improvement in truck turn time at loading bays.

While modest in percentage terms, these gains are significant in high-volume freight operations. For ports handling millions of TEU annually, even a single-digit efficiency improvement can equate to millions of euros in operational savings and substantial reductions in carbon emissions.

A Continuation of North American Success

The European pilots built directly on the prior success at the Port of Los Angeles. There, D-Wave and SavantX’s hybrid system achieved:

• Over 15% improvement in crane utilization.

• Noticeable reductions in fuel consumption from improved sequencing of container movements.

• Strong acceptance by longshore operators and terminal IT managers.

The performance in Los Angeles provided the confidence for European port authorities to greenlight similar pilots, especially given the strain ports faced during the pandemic-driven supply chain disruptions.

D-Wave’s Quantum Annealing Edge

D-Wave’s technology remains one of the most commercially deployable quantum systems. Its Advantage quantum annealer, boasting over 5,000 qubits, is specifically designed for optimization. Unlike gate-model quantum systems still maturing in research labs, D-Wave’s platform allows companies to encode QUBO problems and obtain practical results today.

Hybrid solvers, which combine quantum and classical methods, further enhance the platform’s ability to handle large, noisy, and real-world data sets. This makes D-Wave’s approach uniquely suitable for logistics environments, where problems involve thousands of binary decision variables and require near-real-time decision-making.

SavantX: From Ports to Platforms

SavantX provides the connective tissue between raw logistics data and quantum optimization. Its AI-driven preprocessing algorithms clean, filter, and convert operational inputs into quantum-ready structures. Additionally, SavantX develops the user interfaces that allow port operators, truck drivers, and yard planners to interact with quantum-enhanced recommendations without requiring technical expertise.

Beyond maritime shipping, SavantX is already exploring applications in aviation scheduling, rail yard optimization, and warehouse robotics, aiming to build a cross-industry platform for quantum-enhanced logistics.

Policy and Innovation Alignment

The expansion to Hamburg and Rotterdam aligns with broader European Union digital transformation priorities.

• The European Commission’s Digital Europe Programme highlights quantum computing and AI as priority technologies for freight and logistics modernization.

• Germany’s Federal Ministry for Digital and Transport (BMDV) allocated over €100 million for smart port innovation.

• The Dutch Innovation Council identified quantum optimization as a strategic focus for Rotterdam’s modernization projects.

Advisory support for the pilots also came from the European Institute of Innovation & Technology (EIT) and direct cooperation with the Port of Hamburg Authority, underscoring the strategic relevance of the initiative.

Future Roadmap and Challenges

Looking ahead, D-Wave and SavantX plan to:

• Scale deployment to additional European terminals through 2023 and beyond.

• Extend use cases to vessel berth optimization and customs clearance sequencing.

• Incorporate carbon-tracking tools to quantify sustainability benefits.

However, challenges remain:

• Latency management – ensuring quantum-classical processing times are fast enough for live operational environments.

• Operator trust – training staff to interpret and rely on AI- and quantum-generated recommendations.

• Data harmonization – overcoming differences in software formats and standards across European ports.

Why It Matters

The March 9, 2022 announcement stands as a landmark moment in logistics technology. For the first time, quantum-enhanced freight optimization crossed the Atlantic, moving from proof-of-concept in Los Angeles to live trials at Europe’s busiest shipping hubs.

By delivering tangible improvements—however incremental at this stage—the project demonstrates that quantum logistics is no longer a distant research ambition but an operational reality.

Conclusion: From Pilots to Port Standards

The expansion of D-Wave and SavantX’s quantum optimization platform into Hamburg and Rotterdam in March 2022 represents a turning point for freight logistics. Ports, long constrained by computational bottlenecks, now have access to a tool that can dynamically balance cranes, trucks, and container flows with unprecedented efficiency.

While challenges remain in scaling, standardization, and operator adoption, the early European results validate the same conclusion reached in Los Angeles: hybrid quantum-AI solutions can unlock measurable efficiency gains in real-world freight operations.

As supply chains seek resilience, sustainability, and efficiency in a post-pandemic world, quantum optimization is poised to move from experimental pilots to mainstream adoption. The Hamburg and Rotterdam trials suggest a future where quantum systems quietly orchestrate the world’s busiest trade arteries—making global commerce faster, greener, and more reliable.

Tackling Port Congestion with Quantum-Inspired Innovation

The COVID-19 pandemic created unprecedented strains on global supply chains, particularly in Asia-Pacific, where terminal congestion surged to record-breaking levels. Even as vessel schedules normalized, ports continued to face intense pressure to turn ships around faster while handling higher container volumes. One of the most persistent bottlenecks lies in the assignment of quay cranes—the massive machines responsible for loading and unloading containers from vessels.

Quay crane scheduling is deceptively complex. Each crane has specific reach and movement limits, safety zones, and maintenance schedules. Ships vary in length, cargo type, and handling priority. Traditional scheduling relies on heuristic algorithms and operator expertise, but these approaches struggle with real-time disruptions such as late vessel arrivals or unexpected equipment downtime.

Recognizing the limitations of conventional methods, Japanese shipping giant Mitsui O.S.K. Lines (MOL) turned to quantum computing. Partnering with Canadian quantum pioneer D-Wave Systems, MOL initiated a pilot program in Southeast Asia to test whether quantum annealing could deliver measurable efficiency gains in port crane scheduling.

Quantum Annealing for Crane Assignment Optimization

At the heart of the project was D-Wave’s Advantage quantum system, a platform built for combinatorial optimization problems. Unlike gate-based quantum computers, which are still largely experimental, D-Wave’s quantum annealers are commercially available and designed to work on problems expressed as Quadratic Unconstrained Binary Optimization (QUBO).

The MOL pilot formulated crane scheduling as a QUBO problem. Constraints such as crane reach, movement conflicts, ship size, and maintenance windows were encoded into quantum-ready structures. Priority cargo, including refrigerated containers and time-sensitive goods, received weighted optimization values.

The hybrid solver approach—combining classical pre-processing with quantum annealing—was tested across scenarios involving 5 to 20 ships and up to 50 cranes. The goal was to simulate realistic peak conditions in high-traffic ports, where scheduling complexity is most severe. Results were benchmarked against traditional scheduling software currently used in MOL terminals.

Pilot Results and Measurable Gains

The pilot, carried out at a partner terminal in Southeast Asia (kept undisclosed due to commercial agreements), delivered tangible operational improvements:

• 12% reduction in vessel berth time, directly cutting down costly delays.

• 18% improvement in crane utilization, meaning less idle machinery and more efficient task allocation.

• Fewer crane conflicts in overlapping movement zones, reducing downtime caused by safety holds.

• Greater predictability in container discharge and loading, improving downstream truck and rail scheduling.

These measurable gains provided evidence that quantum-enhanced optimization could move beyond theory into real-world maritime logistics. MOL and D-Wave co-published a technical brief and prepared to present findings at the 2022 International Conference on Ports and Shipping Innovation, highlighting the global relevance of the pilot.

Strategic Context for MOL

For MOL, the pilot was not an isolated experiment but part of its larger "Ishin Next" Digital Transformation Program, which aims to build a smarter, greener, and more resilient logistics ecosystem. Key initiatives under this program include:

• Smart port infrastructure, integrating AI, IoT, and blockchain into daily operations.

• Fleet optimization, using advanced analytics to minimize emissions and fuel costs.

• Data-driven decision support, creating real-time visibility across terminal operations.

MOL’s quantum pilot also fits into Japan’s broader innovation push. Supported by the Moonshot R&D program and METI’s incentives for logistics modernization, Japan has emphasized industrial applications of quantum technologies. MOL’s partnership with D-Wave provided a concrete example of this policy vision in action.

Alongside quantum efforts, MOL has active collaborations with NEC on maritime AI systems, IBM Japan on digital twins for vessel routing, and the Port of Yokohama on 5G-enabled automation trials. By layering quantum optimization into this portfolio, MOL positioned itself at the forefront of next-generation logistics.

D-Wave’s Port Logistics Use Case Expansion

For D-Wave, the collaboration with MOL marked an important milestone. The company had previously demonstrated applications in retail supply chain optimization, airline crew scheduling, and vehicle routing in smart cities. Extending into port logistics validated the versatility of quantum annealing for constrained resource allocation problems.

Alan Baratz, CEO of D-Wave, emphasized that maritime logistics presents a “perfect case study” for quantum annealing. Ports involve multiple constraints, dynamic inputs, and enormous solution spaces—conditions where classical heuristics falter but quantum annealing thrives. Unlike gate-model quantum computing projects, which remain experimental, D-Wave’s hybrid systems provided immediate, measurable results.

Industry and Regional Implications

The MOL-D-Wave partnership is emblematic of a wider regional trend. In Southeast Asia and Northeast Asia, ports and governments are investing in quantum-inspired logistics innovation:

• Singapore: PSA International partnered with the Singapore Maritime Institute to investigate AI and quantum optimization for mega-terminal operations.

• Thailand: The Eastern Economic Corridor (EEC) launched digital port testbeds exploring quantum-enhanced planning.

• South Korea: The Port of Busan began simulation pilots with KAIST to study quantum scheduling scenarios.

Japan’s National Strategy for Quantum Technology (2020) explicitly called for industrial-scale deployment. MOL’s pilot delivered a tangible example of how quantum computing can be deployed to solve long-standing challenges in port operations.

Technical Considerations and Challenges

Despite strong results, MOL and D-Wave acknowledged several challenges before scaling:

• System integration: Bridging terminal operating systems (TOS) with quantum solver modules required new data pipelines.

• Runtime performance: Ensuring solver results fit within tight operational cycles was critical.

• Change management: Port operators required training to interpret and trust quantum-generated schedules.

To mitigate these issues, MOL embedded quantum analysts directly into terminal planning teams and conducted joint scenario workshops. This hands-on approach smoothed the learning curve and built operational confidence.

The Road Ahead: From Pilot to Production

Looking forward, MOL outlined an ambitious roadmap:

1. Scaling deployments: Extend quantum optimization to terminals in Japan and Vietnam.

2. Expanding scope: Incorporate container yard optimization and gate-out scheduling into the solver framework.

3. Forming alliances: Launch a port innovation consortium with technology vendors, government agencies, and logistics stakeholders.

The long-term vision is an end-to-end quantum optimization layer that spans vessel berthing, crane allocation, yard management, and intermodal dispatch.

Conclusion: Quantum Enters Maritime Mainstream

The MOL-D-Wave pilot, launched on February 25, 2022, represents one of the most advanced demonstrations of quantum optimization in global maritime logistics. The results showed not only efficiency gains but also a pathway for integrating quantum into daily port operations.

As the shipping industry faces mounting pressure to digitize, decarbonize, and streamline, quantum computing offers an invaluable new toolkit. MOL’s initiative highlights that quantum innovation is no longer a distant frontier but an emerging operational reality.

If scaled successfully, MOL may set the precedent for quantum-powered ports across the Asia-Pacific and beyond, where efficiency and resilience are key to sustaining global trade.

Southeast Asia’s Quantum Entry via the World’s Busiest Port

Singapore’s port is one of the most complex logistical environments in the world, handling more than 36 million TEUs annually, making it one of the top global hubs for maritime trade. Every day, thousands of vessels dock, load, and depart, requiring precise coordination of berth scheduling, yard management, and container dispatching. These processes demand navigating a highly dynamic mix of constraints—vessel arrival delays, unpredictable weather, global shipping congestion, and localized cargo handling bottlenecks.

On February 17, 2022, PSA announced a landmark collaboration with D-Wave Systems, a Canadian quantum computing pioneer. The partnership aimed to investigate whether quantum annealing could offer a new level of efficiency in solving combinatorially intensive scheduling and allocation problems. The central question: Can quantum optimization meaningfully reduce vessel wait times and improve throughput during peak congestion scenarios?

For Southeast Asia, this move represented more than just a trial. It was a signal of entry into the global race for quantum logistics, joining the ranks of North America, Europe, and East Asia in testing how next-generation computation could reshape supply chain systems.

D-Wave’s Quantum Annealing and Maritime Logistics

D-Wave occupies a unique niche in the quantum computing ecosystem. Unlike gate-based systems, which aim for universal quantum computation, D-Wave offers commercially available quantum annealers specialized for discrete optimization tasks. These machines are particularly adept at solving problems expressed as Quadratic Unconstrained Binary Optimization (QUBO) models.

Port logistics, with its resource allocation and scheduling demands, is naturally suited to QUBO formulations. For PSA, the D-Wave team focused on three primary problem sets:

1. Berth scheduling – assigning vessels to available berths in a way that minimizes overall delays while maximizing utilization.

2. Yard crane scheduling – reducing “dead time” in crane operations by optimizing task sequences.

3. Container reshuffling – minimizing unnecessary container relocations while enabling faster access to outbound freight.

This focus reflected PSA’s operational pain points: even small improvements in these areas translate into massive economic gains at global scale.

Pilot Trial Structure and Technical Scope

The pilot, launched in early February 2022, was structured to test realistic, high-stakes scenarios.

• Data Input: Historical congestion data from PSA terminals was ingested to simulate conditions.

• Modeling Approach: Core scheduling challenges were reformulated as QUBO models.

• Solver Deployment: D-Wave’s Leap™ quantum cloud platform was used, integrating quantum annealers with hybrid classical solvers.

• Evaluation Metrics: KPIs included vessel wait time, container dwell time, and equipment idle time.

In practice, the hybrid solver setup outperformed traditional heuristics in peak-load conditions, particularly where the number of valid scheduling combinations ballooned beyond classical tractability.

Measured Results and Observations

While the February 2022 announcement marked the beginning of the pilot, early evaluations already showed promising outcomes:

• Up to 17% reduction in vessel wait time during peak congestion.

• Improved berth utilization, ensuring higher throughput without additional infrastructure.

• Load balancing gains across container-handling equipment, reducing idle cycles.

One of the most significant findings was the ability of the system to manage soft constraints, such as customer service commitments and multi-objective optimization. The hybrid solvers adapted effectively to last-minute changes in ship schedules—an area where classical systems often falter.

The Regional and Global Significance

The PSA–D-Wave pilot was among the first port logistics trials worldwide to directly apply quantum computing. Its implications extend beyond Singapore.

Globally, other initiatives were already in motion:

• Europe: Airbus and Maersk had begun exploring quantum-enhanced logistics.

• North America: The Port of Los Angeles was piloting AI-quantum hybrid models with Google.

• Middle East: KAUST in Saudi Arabia launched logistics-focused quantum studies.

By entering the field, Singapore positioned Southeast Asia as a critical player. Neighboring ports such as Port Klang (Malaysia), Laem Chabang (Thailand), and Busan (South Korea) could follow suit, raising the region’s technological baseline for maritime logistics.

Technology Stack and Methodology

The technical stack for the PSA pilot reflected D-Wave’s hybrid approach:

• Leap Quantum Cloud Service provided access to annealing hardware.

• Ocean SDK enabled modeling of scheduling problems as QUBO formulations.

• Hybrid Solver API combined quantum exploration with classical refinement.

• Data ingestion modules prepared live and historical terminal datasets.

Container flow scenarios were encoded using integer programming techniques. The solver iterated across thousands of potential solutions, narrowing down to high-scoring candidates. These outputs were then benchmarked against PSA’s rule-based planning systems, demonstrating clear advantages under complex conditions.

Strategic and Economic Implications

Singapore’s trial carried several broader implications:

• Supply Chain Resilience: Post-COVID disruptions had highlighted vulnerabilities in port management. Quantum approaches promised faster, more adaptive scheduling under strain.

• National Quantum Roadmap: In 2022, Singapore’s National Research Foundation committed SGD $25 million to quantum R&D, explicitly including logistics applications.

• Green Shipping: Efficient berth and crane scheduling translates into reduced vessel idling, lowering port emissions—a priority in Singapore’s Green Shipping Corridor initiatives.

D-Wave’s Global Logistics Playbook

The PSA pilot was part of D-Wave’s broader logistics strategy in 2022, which included:

• US Postal Service (USPS) – package sorting and routing pilots.

• Volkswagen – traffic flow optimization trials in Beijing and Barcelona.

• Save-On-Foods (Canada) – distribution center scheduling improvements.

These projects underscored D-Wave’s intent to bring quantum annealing into real-world mission-critical operations, moving beyond academic proofs-of-concept.

Port Operations, Reimagined

The PSA experiment highlighted how quantum annealing complements classical systems. Rather than replacing PSA’s terminal operating system (TOS), quantum solvers acted as an augmentation layer, improving performance in complex, high-uncertainty scenarios.

Applications included:

• Dynamic berth scheduling under variable arrival windows.

• Yard crane task allocation to reduce reshuffles.

• Truck dispatch optimization for outbound containers.

This hybrid role makes quantum less disruptive to existing infrastructure while enabling efficiency gains.

Challenges and Areas for Further Development

Despite encouraging progress, the pilot identified several challenges:

1. Legacy Integration: Aligning quantum outputs with PSA’s TOS required new middleware and testing cycles.

2. Solver Runtime Consistency: Larger QUBO instances occasionally strained available resources.

3. Talent Gap: Few professionals in port operations possessed quantum modeling expertise.

To address this, PSA and D-Wave outlined next steps:

• Live tests integrating quantum solvers directly with PSA’s TOS.

• Joint education programs with National University of Singapore and Nanyang Technological University to train quantum-logistics specialists.

• Exploration of custom solver development tailored to port operations.

Outlook: Scaling Quantum at Global Ports

Looking forward, the PSA–D-Wave collaboration may set the stage for scaling quantum logistics globally. With ports increasingly becoming bottlenecks in global trade, the ability to optimize scheduling in near real time is a strategic asset.

By 2025, PSA could become the first major terminal operator to embed quantum solvers into daily workflows. If successful, the model may inspire adoption at other global ports, from Rotterdam to Los Angeles.

Conclusion

The February 17, 2022, announcement of PSA’s partnership with D-Wave was more than a pilot—it was a statement of intent. Singapore, long known as a logistics and innovation hub, positioned itself at the forefront of applying quantum computing in maritime trade.

The early results—reduced vessel wait times, improved berth utilization, and better equipment balance—demonstrated that quantum annealing has a practical role in one of the world’s most complex operational theaters.

While challenges remain in scaling, integration, and talent development, the partnership underscores a clear trajectory: ports of the future will increasingly rely on quantum-enhanced decision-making. For Singapore, this move not only strengthens its competitive advantage but also positions it as a regional leader in quantum logistics innovation, setting a benchmark for global peers.

South Korea made a decisive move in February 2022 to strengthen its logistics resilience and digital transformation through the convergence of quantum technology and supply chain management. On February 17, 2022, the Electronics and Telecommunications Research Institute (ETRI), the nation’s premier ICT research institute, announced a strategic partnership with CJ Logistics, one of South Korea’s largest third-party logistics providers. The collaboration is designed to explore and implement quantum-powered solutions in supply chain operations, emphasizing route optimization, data security, and logistics network adaptability.

This announcement marked a pivotal point in South Korea’s ongoing quest to establish itself as a global leader in quantum technology by 2030. Backed by a $2 billion national R&D plan under the Ministry of Science and ICT, quantum logistics initiatives like this one are viewed as central to securing the nation’s position in a rapidly evolving Asia-Pacific innovation race.

ETRI and CJ Logistics: Joining Forces for Quantum Supply Chain Modernization

The February 2022 partnership is structured as a multi-phase roadmap to integrate quantum technology into CJ Logistics’ nationwide operations. ETRI brings advanced algorithmic and cryptographic expertise, while CJ Logistics contributes its extensive operational footprint, which spans smart hubs, digital twin systems, and last-mile networks across Korea.

The joint focus areas include:

• Route optimization under high-disruption scenarios such as port congestion, geopolitical disruptions, or unexpected demand surges.

• Post-quantum cybersecurity layers to safeguard logistics and shipping data against potential decryption by future quantum computers.

• AI-quantum hybrid simulations capable of modeling supply chain bottlenecks and delivery variances in dynamic, real-world conditions.

By merging ETRI’s technical leadership with CJ’s operational scale, the collaboration aims to not only modernize Korean logistics but also position the nation as a testbed for scalable global quantum logistics solutions.

Quantum Use Cases in Korea’s Logistics Sector

The early focus of the collaboration was highly practical. Instead of pursuing theoretical research detached from industry, the pilot tests concentrated on real logistics pain points, such as:

1. Port of Busan Optimization – Leveraging quantum-inspired solvers to reroute container movements based on berth congestion, truck availability, and vessel delays.

2. Urban Last-Mile Delivery in Seoul – Testing quantum route optimization models for e-commerce parcels, especially under fluctuating traffic conditions and demand peaks.

3. Warehouse Scheduling – Deploying quantum machine learning to dynamically adapt to unexpected surges in parcel processing volumes, particularly during flash sales or pandemic-driven spikes.

These pilots were implemented using simulated quantum hardware paired with hybrid solvers, ensuring measurable results against classical planning tools like Google OR-Tools and real-time traffic APIs.

Quantum Algorithms Deployed

The collaboration deployed a mix of quantum and quantum-inspired algorithms:

• Quantum Approximate Optimization Algorithm (QAOA) – Applied to NP-hard vehicle routing problems involving thousands of constraints.

• Quantum Neural Networks (QNNs) – Used to forecast parcel demand fluctuations and guide delivery capacity planning.

• Lattice-Based Cryptography – Implemented to secure logistics communication systems against quantum-enabled cyberattacks.

CJ Logistics’ digital twin infrastructure, which digitally replicates 38 nationwide smart hubs, served as a foundational testbed. This allowed ETRI to evaluate quantum-augmented logistics decisions in a controlled yet realistic environment.

Key Results from February 2022 Trials

The February pilot phase produced early but promising outcomes:

• 7–9% improvement in delivery time adherence, particularly for congested Seoul delivery zones.

• 15% improvement in resource allocation during e-commerce flash sales, measured in vehicle utilization and staff scheduling efficiency.

• Zero breaches in quantum-encrypted data simulations, confirming the resilience of post-quantum cryptography against simulated attack vectors.

These results, while preliminary, validated the potential for quantum integration to outperform existing logistics optimization systems.

South Korea’s National Quantum Strategy and Logistics Integration

The collaboration directly supports South Korea’s National Quantum Technology Roadmap, unveiled in late 2021. Logistics was identified as a core beneficiary of the initiative, alongside finance, healthcare, and national security.

Key supporting structures include:

• Korea Advanced Quantum Lab (KAQL): A hub fostering startups and universities in applied quantum research.

• ICT Quantum Infrastructure Fund: Grants and subsidies for pilot projects in logistics and communications.

• Export Logistics Corridors: Designated as quantum testbeds, with Busan and Incheon ports prioritized for deployment.

CJ Logistics’ role as a partner in Korea’s “Digital and Green New Deal” further links the quantum agenda to the country’s sustainability and digitalization targets.

International Partnerships and Regional Context

South Korea’s announcement came amid rising competition in the Asia-Pacific quantum logistics race.

• Japan: NTT and Toyota had launched quantum mobility modeling projects in late 2021.

• China: Baidu Quantum Lab began testing logistics-focused quantum simulations in Guangdong province.

• Singapore: Its National Quantum-Safe Network includes logistics monitoring applications for secure cargo flows.

South Korea’s strategy differs by promoting open collaboration, inviting future research exchanges with Europe and the United States while simultaneously competing with regional neighbors.

Technical and Operational Challenges

Despite the progress, February’s trials revealed several challenges:

• Hardware Limitations: South Korea still lacks large-scale, fully operational quantum processors.

• Workforce Gaps: Few logistics engineers are trained to apply quantum algorithms in real-world settings.

• Data Standardization: Real-time logistics datasets are fragmented and must be harmonized to feed quantum systems effectively.

To address these issues, ETRI launched its Quantum Talent Acceleration Program, in partnership with KAIST and Korea University, to prepare a next-generation workforce.

2023–2025 Roadmap: Scaling the Quantum Logistics Testbed

The collaboration set a three-year trajectory for scaling deployment:

• 2023: Expansion of pilots into maritime cargo, with emphasis on customs pre-clearance optimization.

• 2024: Rollout of post-quantum cryptographic layers across CJ’s logistics cloud infrastructure.

• 2025: Live integration of hybrid AI-quantum optimization tools into CJ’s nationwide parcel sorting systems.

Additionally, ETRI began discussions with SK Telecom and Hyundai Glovis to extend the quantum logistics ecosystem into telecom-enabled tracking and automotive shipping.

Strategic Implications for Global Supply Chains

The CJ–ETRI collaboration represents more than a national initiative—it signals a potential global blueprint. Export-reliant economies could replicate this model to enhance logistics resilience, while multinationals may adopt post-quantum security frameworks for freight data.

As global supply chains face mounting pressure from climate policies, geopolitical disruptions, and e-commerce growth, quantum computing could emerge as the differentiator between traditional logistics systems and next-generation networks.

Conclusion: A National Leap Toward Quantum Logistics

The February 17, 2022, announcement marked a major leap forward in the practical application of quantum technology in logistics. By aligning ETRI’s scientific expertise with CJ Logistics’ operational dominance, South Korea positioned itself at the forefront of quantum-enabled supply chain innovation.

If successful, this collaboration could reshape logistics not only in South Korea but across the Asia-Pacific region, setting standards for secure, efficient, and resilient networks in a post-quantum world.

With clear pilot results, a defined roadmap through 2025, and strong national support, the CJ–ETRI initiative demonstrates how quantum computing is moving beyond laboratories and into the beating heart of global trade.

On February 10, 2022, Hyundai Glovis, the global logistics provider under Hyundai Motor Group, announced a pioneering partnership with South Korea’s Electronics and Telecommunications Research Institute (ETRI). The collaboration focuses on applying quantum computing to one of the most complex logistical challenges in the world: maritime shipping optimization.

For decades, global shipping companies have relied on increasingly sophisticated software systems to manage container stowage, route planning, and port scheduling. Yet, even with powerful classical computers, the sheer scale and complexity of maritime logistics often push the limits of traditional optimization methods. With this pilot, Hyundai Glovis and ETRI aim to leverage quantum algorithms to redefine the efficiency and agility of international shipping across the Asia-Pacific region.

A National Push Toward Quantum in Industry

South Korea has set an ambitious agenda to establish itself as a global leader in quantum technology by 2030. The country’s “Digital New Deal” program emphasizes the integration of advanced computing into industrial applications, and logistics is now emerging as a strategic sector.

Hyundai Glovis, already recognized as a leader in automotive and industrial freight transport, is stepping forward as a testbed for quantum-enabled operations. Partnering with ETRI, the government-funded research institute known for its breakthroughs in ICT and communications, the company has committed to co-developing next-generation quantum algorithms specifically tailored for supply chain and logistics challenges.

The partnership’s stated goals include:

• Designing quantum algorithms for cargo stowage optimization.

• Modeling multi-port route scheduling through quantum-enhanced decision-making frameworks.

• Developing a quantum logistics sandbox to simulate and test hybrid classical-quantum scenarios.

By combining Hyundai Glovis’s operational expertise with ETRI’s quantum research capabilities, the project stands as a flagship example of South Korea’s national commitment to the industrial application of quantum technologies.

Maritime Logistics: A Complex Optimization Problem

Maritime logistics is one of the most intricate optimization domains in the world. A modern cargo ship can carry over 10,000 containers, each with specific characteristics such as weight, size, cargo type, delivery priority, and hazardous material classifications. Planning how to stow these containers is not a straightforward task; it involves satisfying stability rules, safety regulations, and delivery schedules while minimizing handling and fuel costs.

The complexity is compounded when ships make multiple port calls. Scheduling which containers are offloaded first, how cranes are used, and when vessels arrive at congested ports requires solving large-scale combinatorial problems that quickly overwhelm even the most advanced classical algorithms.

Traditional approaches use heuristic methods or approximation techniques, but these often fail to deliver optimal results in real time. Quantum computing—particularly methods such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolvers (VQE)—offers new ways to explore vast solution spaces more efficiently than classical algorithms.

By integrating quantum-inspired approaches into logistics, companies like Hyundai Glovis hope to transform operational performance in ways that were once deemed unattainable.

Inside the Pilot: Quantum Maritime Simulation Platform

The February 2022 pilot is centered around the creation of a maritime quantum logistics simulation platform. This platform is designed to mirror real-world cargo flows across some of East Asia’s most critical shipping corridors, including:

• Busan Port – South Korea’s largest port and one of the top 10 globally.

• Yokohama and Osaka – major Japanese automotive logistics hubs.

• Qingdao and Shanghai – vital Chinese shipping centers.

The simulator enables researchers to test scenarios such as:

• Dynamic container assignment based on vessel stability, priority shipments, and hazardous cargo.

• Optimized port call sequencing to reduce idle time and port congestion.

• Trade-off modeling between fuel consumption and delivery times, helping shipping companies strike a balance between cost efficiency and sustainability.

The system uses ETRI’s domestic quantum emulators in tandem with open-source platforms like Qiskit and PennyLane, ensuring that the algorithms are hardware-agnostic while remaining scalable to future quantum processors.

Early Results and KPIs

Although still in its simulation stage, the pilot has already delivered measurable improvements:

• 14% reduction in container reshuffling time during stowage planning.

• 8% increase in delivery schedule adherence across multi-port shipping routes.

• 2.3x faster performance of prototype quantum-inspired algorithms compared to classical metaheuristics in specific case studies.

Hyundai Glovis emphasized that while current qubit counts are insufficient for fully scaling these solutions, even hybrid approaches are proving to be more effective than purely classical methods. These results highlight the immediate value of quantum-inspired logistics, even before the arrival of fault-tolerant quantum hardware.

Strategic Implications for Hyundai Glovis

Hyundai Glovis has publicly set a goal of becoming the world’s most digitally advanced logistics carrier by 2025. Its entry into quantum logistics is a direct extension of this vision.

The benefits of quantum-enabled logistics for Hyundai Glovis could include:

• Improved fleet utilization for its roll-on/roll-off (Ro-Ro) shipping operations.

• Enhanced competitive edge in vehicle delivery times and shipping costs.

• Stronger sustainability metrics, achieved through optimized fuel consumption and minimized idle times.

These align closely with Hyundai Motor Group’s broader push into smart mobility, electrification, and green transportation strategies. By integrating quantum logistics into its operations, Hyundai Glovis positions itself at the intersection of digital transformation and sustainable shipping.

ETRI’s Role in Korea’s Quantum Ecosystem

ETRI plays a central role in South Korea’s quantum roadmap. As a founding member of the national quantum task force, the institute actively promotes industrial adoption of quantum technologies.

In the logistics sector, ETRI sees quantum computing as a transformative tool to:

• Reduce energy waste and emissions across long-haul shipping.

• Enhance digital twin modeling for ports and shipping terminals.

• Develop post-carbon strategies for Korea’s maritime economy.

The institute is also advancing related technologies such as quantum-secure communications for maritime satellite systems and quantum edge computing for on-board decision-making in vessels.

Asia-Pacific Quantum Logistics Race Heats Up

Hyundai Glovis’s announcement reflects a broader trend across Asia. Regional shipping and logistics leaders are entering the quantum race to secure long-term efficiency and sustainability advantages.

• NYK Line (Japan) is working on quantum-enhanced models to mitigate port congestion.

• COSCO (China) is testing AI-driven quantum optimization for intermodal synchronization.

• PSA Singapore is experimenting with qubit-based load-balancing for cranes and automated guided vehicles (AGVs).

These developments signal a regional pivot toward quantum-powered logistics, with governments and industry leaders investing heavily to maintain competitiveness in global trade.

Future Roadmap and Integration

Looking ahead, Hyundai Glovis and ETRI have outlined a phased roadmap for their quantum logistics initiative:

• Late 2022: Integration of the simulator with live container tracking systems.

• 2023: Algorithm validation using real stowage plans from Busan operations.

• 2024: Deployment of hybrid quantum-classical optimization tools in select operational settings.

Discussions are also underway with hardware vendors specializing in diamond-based and trapped-ion quantum processors, with the aim of building early testbeds capable of supporting real-world logistics workloads. Additionally, Hyundai Glovis and ETRI are preparing a quantum curriculum to train logistics engineers in this emerging field.

Conclusion: A New Quantum Horizon for Shipping

The Hyundai Glovis–ETRI partnership marks a milestone not just for South Korea, but for the broader logistics industry. For the first time, one of the world’s largest automotive shippers is actively testing quantum computing in a core operational domain.

The early simulation results point to tangible efficiency gains, while the strategic roadmap suggests a serious commitment to scaling quantum logistics into full operational practice. As regional competitors like Japan, China, and Singapore advance parallel efforts, the Asia-Pacific is fast becoming the epicenter of quantum-driven logistics innovation.

If successful, Hyundai Glovis’s quantum initiative could pave the way for smarter, greener, and more resilient shipping networks, helping reshape how global trade moves across oceans in the coming decades.

In a landmark announcement on January 27, 2022, IBM and Maersk unveiled a pioneering pilot project designed to integrate post-quantum cryptography (PQC) into their blockchain-based TradeLens platform. This marks one of the first live applications of quantum-safe cryptography in global logistics, setting a precedent for supply chain stakeholders worldwide who must begin preparing for the looming risks of quantum-powered cyberattacks.

Quantum-Resistant Supply Chains: From Theory to Application

The logistics industry has often been at the forefront of adopting emerging technologies, from containerization in the mid-20th century to blockchain in the 21st. Yet, the arrival of quantum computing introduces both opportunities and existential challenges. On the one hand, quantum algorithms could optimize shipping routes, container loading, and port scheduling. On the other, quantum computers pose a direct threat to classical cryptographic methods that underpin trust and security in international trade.

At the heart of the concern is Shor’s algorithm, a quantum procedure capable of factoring large integers exponentially faster than classical algorithms. This renders widely used public-key systems like RSA and elliptic-curve cryptography (ECC) vulnerable. For global trade, where every bill of lading, digital certificate, and customs clearance depends on these systems, the stakes could not be higher.

IBM and Maersk’s move to implement PQC within TradeLens demonstrates a shift from theoretical caution to practical application. By doing so, they are not only protecting their own digital infrastructure but also setting a roadmap for the entire logistics ecosystem.

Inside the IBM-Maersk Post-Quantum Pilot

TradeLens, launched in 2018 as a joint IBM-Maersk venture, is a blockchain-based logistics platform that facilitates the exchange of data across shippers, ports, freight forwarders, and customs agencies. By 2022, it was already handling over 15 million shipping events weekly across more than 150 active nodes.

For the PQC pilot, IBM and Maersk selected CRYSTALS-Kyber, a lattice-based encryption algorithm and one of the leading candidates in the U.S. National Institute of Standards and Technology (NIST) Post-Quantum Cryptography standardization process. The algorithm was deployed in selected TradeLens data channels to secure sensitive transaction metadata, including shipment status updates and customs documentation.

The pilot used a hybrid model: CRYSTALS-Kyber was combined with Advanced Encryption Standard (AES) protocols to ensure backward compatibility with existing systems. The trial was conducted across key international trade routes linking Hamburg, Singapore, and Dubai—three critical hubs for containerized freight.

Why Post-Quantum Cryptography Now?

Critics might argue that large-scale quantum computers are still years, perhaps decades, away. But global logistics systems, often built on infrastructure expected to last 20–30 years, cannot afford to wait. A concept known as "harvest now, decrypt later" adds urgency: adversaries could capture encrypted data today, store it, and decrypt it once quantum computers become powerful enough. For supply chains that handle sensitive cargo—from pharmaceuticals to defense components—this represents a critical vulnerability.

IBM underscored that the pilot was not merely about risk avoidance. It was about preserving trust. As logistics systems increasingly adopt automation—digital customs clearance, automated container handling, AI-driven trade compliance—ensuring these processes remain secure is vital to maintaining confidence in global commerce.

Policy Alignment and Global Industry Context

The PQC trial dovetails with multiple global initiatives:

• NIST PQC Process: NIST’s multi-year competition to identify post-quantum standards has placed Kyber as a leading encryption candidate. IBM and Maersk’s adoption signals early alignment with these standards.

• European Union Cybersecurity Guidance: ENISA has urged industries managing critical infrastructure, including shipping and energy, to begin planning for PQC migration.

• Maritime Cybersecurity Policy: The International Maritime Organization (IMO) has called on member states to strengthen maritime digital infrastructure, identifying quantum resilience as a long-term priority.

By conducting this trial, IBM and Maersk are positioning themselves not only as industry innovators but also as standard-setters who can influence regulatory frameworks and compliance norms.

Pilot Findings and Early Performance Indicators

While detailed technical results remain confidential, preliminary findings were shared by both companies. PQC-encrypted transactions introduced less than five milliseconds of additional latency—an acceptable overhead for most logistics processes. End-to-end interoperability between PQC and classical systems was achieved, ensuring that the new security protocols did not disrupt ongoing operations.

Crucially, customs authorities in Hamburg, Singapore, and Dubai were able to verify PQC-encrypted documentation without modification to their existing IT systems, highlighting the hybrid model’s practicality. Maersk’s Chief Information Officer suggested that broader rollout could begin by late 2022, pending continued successful testing.

Expanding the Post-Quantum Security Movement

The IBM-Maersk pilot reflects a broader industry shift. DB Schenker has been working with IBM on PQC trials for European rail logistics. FedEx commissioned an audit of quantum risks across its North American route planning software. The Port of Rotterdam, one of Europe’s busiest gateways, announced a PQC feasibility study in late 2021. These moves highlight that PQC is no longer an abstract academic exercise—it is a priority in enterprise IT strategy.

Technical Lessons Learned

The pilot surfaced important technical insights:

• Key Management Complexity: Lattice-based cryptography requires larger key sizes, creating challenges in storage and management.

• Training Gaps: Logistics IT engineers needed targeted training on PQC libraries to avoid deployment errors.

• Hybrid Cryptographic Models as a Bridge: The use of both PQC and conventional encryption ensured system continuity while gradually transitioning to quantum-safe methods.

These lessons provide a foundation for other logistics operators considering PQC adoption, lowering the barrier to entry.

Looking Ahead: Secure Trade in a Quantum Future

The integration of PQC into TradeLens marks a pivotal step toward a secure digital future for global logistics. It represents more than an isolated trial; it is a signal that the shipping industry is beginning the difficult but necessary journey of transitioning away from vulnerable cryptographic systems.

The pilot illustrates that PQC is not only feasible but practical for live operations at scale. With minimal latency and strong interoperability, it addresses the critical concerns of industry stakeholders. Moreover, by choosing high-traffic trade routes, IBM and Maersk have demonstrated PQC’s potential in real-world, high-stakes environments.

Conclusion

IBM and Maersk’s January 2022 PQC pilot is a milestone in the convergence of quantum computing and logistics security. By proactively addressing the looming cryptographic risks posed by quantum computers, the companies have taken a leadership role in safeguarding global trade. Their efforts demonstrate that quantum resilience in logistics is not a far-off ambition—it is an immediate necessity.

If scaled, this initiative could serve as a blueprint for ports, carriers, and customs agencies worldwide, ensuring that global supply chains remain both efficient and secure in the quantum era.