Quantum Articles 2021

Background: The Challenge of Modern Port Logistics

As global supply chains grew more strained in 2021, port congestion became a visible chokepoint. Ships waited days for berths. Containers clogged terminals. Truck turnarounds slowed. Ports like Yokohama, Japan’s second-largest, faced immense pressure to modernize operations—especially with container volumes rebounding post-COVID.

Key logistical challenges included:

• Container dwell time exceeding targeted windows

• Inefficient berth and crane scheduling

• Poor yard stacking order, causing excessive reshuffling

• Inflexible intermodal transfer planning between trucks and ships

Traditional optimization tools struggled with the NP-hard nature of these problems—especially as variables surged due to unpredictable ship arrivals, labor shortages, and limited yard space. Enter Toshiba’s quantum-inspired computing approach.

Simulated Bifurcation Machine: Quantum Power Without Quantum Hardware

Toshiba’s Simulated Bifurcation Machine (SBM) is a quantum-inspired optimizer that simulates quantum tunneling behavior using high-speed classical computing. Unlike quantum gate computers or annealers, SBM is available today and is already deployed across Toshiba’s logistics and energy optimization clients.

In the Yokohama Port project, SBM powered a dynamic digital twin—a real-time virtual replica of port assets and workflows. This allowed the port’s logistics team to:

• Simulate thousands of future scenarios

• Re-optimize plans instantly as ship ETAs, crane availability, or weather conditions changed

• Perform multi-objective optimization balancing throughput, emissions, and service time

Scope and System Integration

The project focused on three key domains of port operation:

1. Container Yard Optimization

• Improved stacking strategy to reduce reshuffling

• Prioritized placement of time-sensitive cargo near transfer lanes

1. Berth and Crane Scheduling

• Dynamic reallocation of berths based on ship size, cargo type, and predicted dwell time

• Synchronized crane deployment to minimize idle motion

1. Intermodal Truck Coordination

• Real-time slot booking optimization for outbound trucks

• Minimized truck idle time via predictive container readiness estimates

The system was integrated with:

• Yokohama Port’s TOS (Terminal Operating System)

• Real-time weather and ETA feeds

• Trucking company booking portals

• AI vision systems for yard monitoring

Digital Twin as a Quantum Optimization Interface

At the heart of the system was the quantum-enhanced digital twin—an interactive simulation environment updated every 5 seconds with:

• Container positions

• Crane availability

• Vessel berthing status

• Truck queue metrics

The SBM ran optimization loops continuously, updating optimal decisions for:

• Where to place new incoming containers

• Which crane to assign to a vessel next

• How to assign trucks to lanes and docks for fastest turnarounds

These decisions were visualized via a digital dashboard used by port managers and terminal operators, allowing human override and “what-if” scenario testing.

Key Results from December 2021 Deployment

During the December trial period (12/1–12/21), the system delivered measurable improvements:

Metric

Improvement

Avg. container dwell time

↓ 17.3%

Crane idle movement (non-load lifts)

↓ 22.8%

Truck turnaround time

↓ 19.5%

Yard reshuffle operations

↓ 25.2%

Berth utilization efficiency

↑ 15.1%

Port of Yokohama officials noted smoother traffic flow within the yard, fewer truck delays at gate checkpoints, and reduced overall container handling costs.

Sustainability and Emissions Impact

By improving crane efficiency and reducing unnecessary container reshuffling, the system helped Yokohama Port:

• Lower fuel consumption for rubber-tired gantry cranes (RTGs)

• Reduce truck idling outside the port perimeter

• Increase throughput per hour, improving vessel servicing efficiency

These enhancements aligned with the Ministry of Land, Infrastructure, Transport and Tourism (MLIT)’s Port Decarbonization Roadmap, and earned the pilot project a SmartPort Innovation Award from Japan’s port authority network.

Toshiba’s SBM vs. Traditional Solvers

Traditional solvers like mixed-integer programming (MIP) or rule-based heuristics struggled with the dynamic, multi-layered complexity of port operations—particularly when facing disruptions like late vessel arrivals or typhoon warnings.

Toshiba’s SBM offered:

• Near real-time computation (<3 seconds) even with 10,000+ variable configurations

• Better handling of constraint-rich environments (e.g., hazardous cargo separation, customs pre-clearance, crane service radius)

• Scalability to include future variables such as carbon emissions pricing or AI-driven demand prediction

Collaboration Model and Expansion

The project was a three-way collaboration between:

• Toshiba Digital Solutions, providing the optimization engine

• Yokohama Port Authority, managing digital infrastructure and data feeds

• Yokohama Container Terminal (YCT), handling operations at on-site berths and yards

Following the success of the pilot, Toshiba began discussions with:

• Nagoya Port and Kobe Port for regional deployment

• Singapore Maritime and Port Authority (MPA) for feasibility assessments

• Integration with Japan’s national SmartPort cloud initiative

Global Port Trends and Quantum Adoption

Ports worldwide are beginning to explore quantum and quantum-inspired solutions, including:

• Port of Rotterdam: Quantum-safe encryption and logistics scenario modeling

• Port of Los Angeles: AI routing simulations with planned quantum trials

• Hamburg Port Authority: Research with Fraunhofer on quantum-enhanced berth planning

Yokohama’s SBM-based digital twin is one of the first operational, live-deployed systems using quantum-inspired computing in a major port.

Key Challenges and Lessons Learned

While successful, the pilot surfaced several operational lessons:

• Training gaps: Port staff needed extensive upskilling to interpret digital twin insights

• Data quality: Inconsistent tagging and GPS lags affected optimization accuracy

• Change management: Shifting from rule-of-thumb scheduling to algorithmic decision-making faced initial cultural resistance

Toshiba responded by:

• Adding explainability layers to the optimizer (e.g., “Why this crane was assigned here”)

• Offering simulations for operators to test their intuition against algorithmic suggestions

• Rolling out modular deployments, letting teams adopt optimization gradually

Looking Ahead: Quantum-Enhanced Smart Ports

Building on December 2021’s success, Toshiba and Yokohama Port have outlined next steps:

• Extend optimization to energy management, including battery storage and peak-shaving for RTG cranes

• Integrate quantum-safe cybersecurity protocols into data sharing between vessels, customs, and yard operations

• Add AI forecast layers for weather, cargo demand, and vessel delays to guide preemptive optimizations

The broader vision is to establish a national network of quantum-optimized smart ports, sharing learnings, algorithms, and simulation capabilities across Japan.

Conclusion: Quantum-Inspired Logistics Comes to Shore

The deployment of Toshiba’s SBM system at Yokohama Port represents a pivotal step in the convergence of quantum computing and global logistics. By enhancing berth scheduling, container stacking, and truck flow in one of Asia’s busiest gateways, this initiative proves that quantum-inspired optimization is not just theoretical—it is operational, scalable, and impactful.

As global trade rebounds and port congestion continues, these technologies will become critical in building resilient, efficient, and sustainable maritime supply chains.

The Quantum Need in Cold Chain Logistics

Cold chain logistics is among the most complex subsectors of supply chain management due to stringent requirements for temperature stability, rapid transit, and regulatory compliance. Small deviations can result in spoiled goods or noncompliance penalties—especially for pharmaceutical or vaccine transport.

Mitsubishi Logistics, one of Japan’s largest logistics providers, manages over 70 temperature-controlled warehouses and hundreds of refrigerated trucks. The company identified several persistent challenges:

• Routing inefficiencies that led to fuel waste and delayed deliveries

• Load balancing issues in trucks and containers with multiple temperature zones

• Storage space optimization in urban cold warehouses with fluctuating inventory volumes

Conventional routing and planning systems struggled to meet the high-dimensional nature of these constraints. That’s where quantum-inspired optimization offered a new approach.

Fujitsu’s Digital Annealer: Bringing Quantum Speed to Today’s Hardware

The pilot was powered by Fujitsu’s Digital Annealer, a specialized computing architecture that mimics quantum annealing behavior on classical processors. Unlike general-purpose quantum computers, the Digital Annealer is:

• Stable and commercially deployable

• Exceptionally fast for solving combinatorial optimization problems

• Usable with standard data inputs and accessible via API integration

Fujitsu designed a logistics optimization suite built on the Digital Annealer, tailored to cold-chain use cases.

Key modules included:

• Temperature-sensitive route planning considering delivery time windows, truck capacity, and refrigeration zones

• Cold warehouse space optimization, maximizing throughput while ensuring FIFO (first-in-first-out) inventory practices

• Multi-compartment truck loading, optimizing how to assign goods with different temperature ranges to shared vehicle space

The Pilot Scope: Tokyo–Osaka Perishable Goods Corridor

The initial pilot covered deliveries between Tokyo and Osaka, one of Japan’s busiest domestic logistics corridors. The focus was on:

• Seafood and fresh produce sourced from Tsukiji Market and bound for western Japan

• Biopharmaceutical products delivered to hospitals and clinics

• Warehousing at MLC’s Yokohama and Kobe cold storage facilities

Each delivery required:

• Adherence to strict temperature limits (−20°C to +8°C)

• Minimization of transit time to preserve freshness

• Compliance with Japan’s national Good Distribution Practice (GDP) standards

Optimization Objectives and Constraints

The optimization engine had to process:

• Real-time traffic data from Japan’s national highway network

• Perishable item properties (e.g., required humidity, light protection)

• Warehouse and vehicle availability

• Delivery urgency rankings based on perishability and contract SLA terms

Fujitsu encoded these constraints into a QUBO (Quadratic Unconstrained Binary Optimization) model, which the Digital Annealer solved iteratively. The system could evaluate millions of route and load permutations in seconds, providing near-real-time suggestions to Mitsubishi Logistics planners.

Results from the Quantum Pilot

Over a 30-day test period in December 2021, the pilot yielded measurable operational improvements:

Metric

Improvement

Average delivery time

↓ 14.6%

Fuel consumption per delivery

↓ 11.2%

Cold storage utilization efficiency

↑ 18.4%

SLA compliance (on-time + condition)

↑ 9.7%

Additionally:

• Truck idle time was reduced, especially for multi-drop routes with staggered appointments.

• Spoilage rate for seafood and produce decreased by nearly 5%, attributed to tighter temperature-time adherence.

MLC logistics managers noted that the system allowed for dynamic replanning during peak periods, such as December’s holiday-driven food distribution spike.

Integration into Daily Operations

The pilot’s success encouraged MLC to expand the use of quantum-inspired systems in daily planning workflows.

By early 2022, they began:

• Training dispatchers and planners to use the Fujitsu dashboard

• Integrating the optimization engine into their TMS and WMS platforms

• Deploying mobile route updates to refrigerated truck drivers in real time

The architecture supported full API-based integration, meaning planners could run "what-if" simulations when weather, traffic, or labor availability changed without delaying decisions.

Strategic Implications for Japan’s Cold Chain Industry

Japan has a highly advanced cold chain system, but the integration of quantum-inspired tools marks a shift toward cognitive logistics—where decisions are dynamically optimized using advanced computation rather than static rules.

Mitsubishi Logistics’ deployment sets a precedent in several ways:

• It demonstrates quantum ROI today, without needing error-corrected quantum computers

• It showcases how edge constraints (temperature, freshness, space) can be modeled in combinatorial frameworks

• It opens the door for other sectors—like vaccine logistics, livestock transport, and frozen food exports—to benefit from similar optimization

Regulatory and Environmental Alignment

The pilot aligns closely with Japanese policy initiatives:

• The Green Logistics Partnership Conference (GLPC) encourages CO₂ reduction in transport

• The Smart Logistics Innovation Program from METI supports AI and advanced optimization in freight

• MLC is a signatory to Japan’s 2050 decarbonization roadmap, seeking logistics emissions reductions through better load consolidation and routing

By improving route efficiency and cold storage turnover, the Fujitsu system helps MLC lower its carbon footprint without compromising on product safety or timing.

Global Context and Competitive Advantage

Japan’s use of quantum-inspired logistics is now ahead of many global cold chain players. In 2021–22:

• Americold in the U.S. began exploring quantum routing pilots with D-Wave but had not moved to deployment

• DB Schenker focused on temperature-compliant cryptography for medical shipments

• Alibaba’s Cainiao applied AI optimization but had not yet adopted quantum-inspired techniques

Fujitsu’s Digital Annealer gives MLC a first-mover advantage in near-real-time perishable goods planning, positioning it as a technology leader not only in Japan but globally.

Challenges and Future Development

Despite the success, several challenges remain:

• Solver tuning was needed to balance planning quality with computational speed

• Data integration was slowed by disparate formats across warehouse systems and GPS sources

• Planner confidence required time; initially, some operators were hesitant to trust algorithmic suggestions over manual intuition

Fujitsu responded by adding "confidence indicators" that explain how route or load decisions were derived, boosting user trust.

Future plans include:

• Expanding coverage to refrigerated rail and air freight nodes

• Integrating AI forecasting (e.g., spoilage risk or demand surges) upstream of quantum optimization

• Potential export of the system to ASEAN logistics subsidiaries

Conclusion: A Quantum-Optimized Cold Chain

Mitsubishi Logistics and Fujitsu have demonstrated that quantum-inspired optimization can deliver real, tangible benefits in one of the most sensitive logistics domains—cold chain.

By combining classical logistics knowledge with advanced computational power, they’ve redefined what’s possible in planning for perishables. The December 2021 pilot not only proved technical feasibility but also showed economic and environmental value—setting a new standard for logistics innovation in Japan and beyond.

Introduction: Rail Logistics Meets Quantum Algorithms

As North America’s largest rail freight carrier by revenue, Canadian National Railway manages over 32,000 kilometers of track spanning Canada and the central United States. Its complex logistics network includes container ports, inland intermodal terminals, and hundreds of trains in motion daily—each requiring precise coordination of cargo, locomotives, and personnel.

Traditional optimization methods, even when enhanced with AI, often struggle with the combinatorial scale and dynamic uncertainty in railway logistics. In response, CN partnered with Multiverse Computing, a leading quantum software firm known for its Singularity platform—designed to run quantum algorithms on both quantum and classical systems. The initiative targeted quantum-inspired solutions for two high-impact use cases:

• Rail yard congestion minimization and train sequencing

• Rolling stock (wagon) allocation and routing optimization

Multiverse Computing’s Technology Stack

Multiverse Computing, based in San Sebastián, Spain, is best known for developing quantum algorithms that are executable on today’s classical hardware, a strategy that enables immediate commercial applications without waiting for fault-tolerant quantum machines.

The company’s flagship platform, Singularity, supports:

• D-Wave and IonQ quantum backends for annealing- and gate-based processing

• Quantum-inspired tensor network solvers

• Hybrid classical-quantum workflows that combine constraint solvers with quantum-assisted decision trees

For CN, this meant deploying optimization solvers that could handle:

• Large-scale combinatorial problems (e.g., train arrangement at congested depots)

• Operational constraints such as maintenance slots, weather impacts, and regulatory windows

• Real-time input streams from rail sensors, GPS, and fleet management software

Phase One: Yard Scheduling Pilot at Winnipeg Terminal

The initial pilot focused on one of CN’s busiest hubs: the Winnipeg rail yard, which sees hundreds of freight cars reclassified daily.

Key challenges:

• Scheduling trains to minimize yard congestion and maximize throughput

• Reducing delays in assembling outbound trains with multiple origins and destinations

• Accounting for rolling stock availability, crew shifts, and maintenance windows

Multiverse developed a quantum-inspired scheduling engine modeled after Quadratic Unconstrained Binary Optimization (QUBO) frameworks commonly used in D-Wave architectures. The algorithm processed:

• Real-time car arrival/departure data

• Yard track availability

• Time-window constraints on train dispatches

Results:

After six weeks of simulated testing and one week of shadow deployment:

• Yard congestion was reduced by 17%, as measured by average car dwell time

• Train sequencing improved throughput by 12%

• Operational conflicts (e.g., double allocations or track overlaps) dropped by 22%

Although still in simulation mode, CN noted these improvements could equate to millions in annual savings if rolled out at scale.

Rolling Stock Allocation and Routing: Quantum vs. Classical

The second part of the pilot evaluated quantum methods for rolling stock utilization—specifically how CN assigns available freight wagons across a network of customer routes and industrial sidings.

Challenges include:

• Matching wagon types (e.g., tankers, containers, grain hoppers) to specific cargo

• Preventing deadheading (empty returns) across long distances

• Balancing network load and maintenance schedules

Multiverse’s team modeled this as a constrained optimization problem, incorporating:

• Real-time fleet status from CN’s TMS and rail asset trackers

• Routing priorities based on customer SLAs

• Maintenance scheduling requirements

The solver employed a hybrid system:

• Simulated annealing as the core optimizer

• Quantum-enhanced probabilistic subroutines for non-linear constraint resolution

Comparative Findings:

• The hybrid quantum algorithm outperformed CN’s legacy scheduler by 8–11% on total distance traveled per wagon

• Empty car repositioning was reduced by 7.5%

• Fleet availability improved, with fewer idle wagons sitting unused between jobs

These results suggested not only cost savings but also enhanced carbon efficiency, as more effective wagon utilization meant fewer emissions per ton-kilometer shipped.

Industry Context: Rail Meets Quantum Globally

CN’s quantum initiative is part of a growing global trend:

• DB Cargo in Germany announced in 2021 it was exploring quantum scheduling for cross-border freight

• Indian Railways launched a program with TCS and QNu Labs for post-quantum encryption in control systems

• BNSF Railway has been studying optimization enhancements via machine learning and is reportedly evaluating quantum tech for use in intermodal hubs

The rail sector, with its heavy reliance on network design, scheduling, and equipment allocation, is particularly well suited to combinatorial optimization—a domain where quantum and quantum-inspired techniques shine.

Strategic Significance for CN

For CN, the pilot represented more than a technical trial:

• It signaled a shift from AI-only to hybrid quantum-classical architectures in logistics software

• Positioned CN as a first mover among North American carriers in adopting quantum optimization

• Opened the door for further collaborations with startups and universities through Canada's National Quantum Strategy

Executives at CN’s Innovation Group emphasized that this pilot was part of a broader 5-year roadmap to explore:

• AI + Quantum coordination for network rescheduling after weather disruptions

• Energy-efficient logistics, pairing electrified freight corridors with route optimizers

• Freight security, including post-quantum encryption for onboard communications

Integration and Next Steps

Following the December 2021 pilot, CN and Multiverse outlined a Phase Two:

• Expand yard scheduling to Toronto and Chicago terminals

• Begin integration of Singularity platform into CN’s centralized Rail Operation Dashboard

• Host workshops with operational staff to co-develop user-facing optimization tools

Longer term, CN aims to:

• Connect optimization engines with real-time control systems (yard switchers, signal boxes)

• Benchmark quantum optimization ROI vs. classical AI in monthly KPIs

• Participate in Canada’s Quantum Technologies Supercluster, helping shape national priorities

Challenges and Considerations

While the pilot showed promise, CN acknowledged limitations:

• Solver speed was a bottleneck in high-volume situations

• Integration with legacy IT systems (some decades old) required middleware development

• Organizational change management—train dispatchers and logistics planners required retraining to interpret quantum-derived route outputs

Multiverse responded by offering explainability modules, translating solver decisions into natural language rationales and actionable steps.

Conclusion: Laying Tracks for a Quantum Future

CN’s partnership with Multiverse Computing is a case study in the practical application of quantum optimization in complex freight logistics. By focusing on yard operations and wagon routing, two high-impact, high-cost areas, CN demonstrated that quantum-inspired techniques can yield measurable, operational results today—not just in theoretical projections.

This initiative sets the stage for broader industry adoption, where real-world gains in throughput, emissions, and cost-efficiency become the proving ground for quantum value.

Border policy shifts and customs backlogs

Racing Against Time: Logistics in High-Speed Motorsports

In the world of motorsport, supply chain precision is not just critical—it’s existential. Teams like Andretti Autosport operate under extreme time pressure, moving equipment, vehicles, and personnel across continents with minimal error tolerance. The complexity is compounded by:

• Tight event schedules across global circuits

• Changing regulations, weather, and travel constraints

• Just-in-time delivery of specialized car parts and gear

Traditional analytics systems are limited in how quickly they can process and adapt to multivariate disruptions in real time. This made motorsport logistics an ideal testbed for Zapata Computing’s emerging quantum-classical hybrid machine learning models.

The Collaboration: Zapata + Andretti = Quantum-Speed Logistics

Zapata Computing, a leader in quantum algorithm development, partnered with Andretti Autosport to design a quantum-enhanced predictive logistics framework. The goal: to improve forecasting, disruption handling, and operational efficiency in Andretti’s event-driven logistics network.

Key focus areas of the pilot included:

• Predictive delay modeling for shipments across different race locations

• Real-time rescheduling of equipment delivery based on changing customs or weather inputs

• Dynamic spare parts allocation from distributed inventory pools

• Quantum ML for anomaly detection in sensor data from shipping crates and transport vehicles

The pilot aimed to assess the ability of quantum algorithms to outperform classical models in forecasting and mitigation speed, especially during event crunch windows.

Zapata’s Quantum Machine Learning Stack

Zapata brought its proprietary Orquestra® platform to the pilot—a workflow orchestration system that integrates quantum and classical computing resources. Orquestra allows users to run hybrid pipelines across different compute backends, leveraging the best of both worlds.

In this case, Zapata deployed:

• Variational quantum classifiers (VQC) for probabilistic forecasting of delivery delays

• Quantum-enhanced anomaly detection models for telematics data streams

• Tensor network simulations to evaluate hybrid performance under noisy intermediate-scale quantum (NISQ) conditions

The workflows were benchmarked against Andretti’s conventional analytics stack to test speed, accuracy, and adaptiveness under uncertainty.

Pilot Structure and Results

The pilot program was structured around three race weekends in November 2021, where both real and simulated logistics data were tested:

Race Locations:

1. St. Petersburg, Florida

2. Austin, Texas

3. Mexico City, Mexico

Input Data Streams:

• Weather and customs data

• IoT signals from containers and flight trackers

• Maintenance and pit crew scheduling dependencies

• Inventory data from U.S. and Mexico-based warehouses

Output Metrics:

• Forecasting accuracy of disruptions

• Lead time for rescheduling

• Accuracy of predictive parts allocation

• Confidence intervals for delay scenarios

Reported Gains:

• 18% faster anomaly detection compared to baseline AI models

• 11% improvement in shipment rescheduling accuracy

• 23% better inventory allocation performance when paired with real-time trackside logistics inputs

These results validated the early utility of quantum workflows in predictive logistics—especially where narrow margins and fast adaptation are mission-critical.

A Quantum Edge in Fast-Moving Industries

Andretti Autosport COO Rob Edwards emphasized that quantum forecasting could deliver a meaningful edge in race preparedness and inventory utilization. While early, the pilot showed that hybrid quantum models could absorb more upstream signals—like temperature shifts, customs bottlenecks, or spare part usage forecasts—and return optimized decisions faster than traditional tools.

Zapata CTO Yudong Cao noted that predictive logistics is a natural fit for quantum computing because it’s “a classically hard, nonlinear, and dynamic optimization space”—one where small errors or delays can cause cascading disruptions.

Scaling Roadmap and Broader Applications

Following the successful pilot, Andretti and Zapata outlined a roadmap to expand the scope of quantum logistics use:

2022–2023 Plans:

• Expansion to FIA Formula E and IndyCar events

• Integration with air freight planning for intercontinental races

• Deployment of Zapata’s QML pipelines into Andretti’s logistics control center

• Extension into fleet routing optimization for support vehicles

Zapata also began exploring quantum logistics applications beyond motorsports, including:

• Pharmaceutical cold chain monitoring

• Event logistics for the entertainment industry

• Defense supply logistics in high-risk environments

Each of these scenarios shares the need for rapid adjustment to shifting constraints—a hallmark strength of quantum-classical hybrid modeling.

Industry Implications: Proof of Concept in Agile Environments

This motorsport logistics pilot is a critical inflection point for quantum computing’s role in live logistics:

• High-frequency logistics settings (like racing, fashion shows, or military deployments) require ultra-fast prediction and response cycles

• Quantum machine learning (QML) can handle multi-signal noise, correlations, and conditional dependencies more nimbly

• Hardware-agnostic platforms like Orquestra can run these models even before fault-tolerant quantum processors arrive

It signals to the broader supply chain community that quantum doesn’t have to wait for hardware maturity—it can deliver value today through hybrid deployments.

Quantum Forecasting: A New KPI Frontier

A unique feature of Zapata’s platform is its ability to quantify confidence levels in logistics forecasts. For example:

• Probability of on-time arrival for high-value parts

• Risk curves for customs clearance based on recent inspection patterns

• Adaptive thresholds for intervention in rescheduling

These insights go beyond binary “on-time or not” judgments and help planners make more informed decisions—even under low-certainty conditions.

Andretti’s logistics team noted that this probabilistic insight helped optimize labor deployment and spare part positioning, avoiding both overstocking and shortfalls.

From Track to Factory: Lessons for Broader Supply Chains

While born in the fast-paced world of racing, the lessons from this pilot apply broadly:

• Synchronized predictive logistics is becoming key to efficient global operations

• Quantum-enhanced ML can process more input variables and conditional dependencies than classical systems

• Hybrid quantum-classical architectures are already usable today—even as fault-tolerant quantum hardware remains years away

Industries like manufacturing, aerospace, e-commerce, and healthcare can leverage similar quantum workflows to optimize forecasting, routing, and network resilience.

Policy and Market Context

This partnership aligns with growing interest in applied quantum innovation:

• The U.S. National Quantum Initiative supports private-sector pilots with logistics relevance

• Investors are backing practical-use quantum startups like Zapata, Xanadu, and Quantinuum

• Early enterprise adoption is shifting the focus from pure research to business transformation use cases

Andretti’s willingness to experiment in this space underscores how even niche industries can help validate deep-tech value chains.

Conclusion: A Quantum Start for Agile Logistics

The November 2021 pilot between Zapata Computing and Andretti Autosport demonstrated that hybrid quantum machine learning can enhance predictive logistics in real-world, time-sensitive environments. By increasing forecast precision, adapting to uncertainty faster, and optimizing inventory and transport decisions, quantum computing took a major step from theory into practice.

As Zapata and Andretti scale the model into more race series and scenarios, they offer a roadmap for how logistics leaders across sectors can start their own quantum journeys—not someday, but now.

Background: Cross-Border Supply Chain Vulnerabilities

The U.S., Mexico, and Canada form one of the world’s largest trilateral trade blocs, accounting for trillions in annual goods movement. Yet, cross-border logistics remains vulnerable to a range of disruptions:

• Border policy shifts and customs backlogs

• Labor shortages in distribution centers

• Natural disasters or regional infrastructure outages

To address these growing risks, Zapata Computing and Accenture launched a quantum modeling partnership to analyze cascading effects of disruptions and optimize dynamic rerouting, supplier substitution, and inventory distribution across cross-border corridors.

Why Quantum Techniques?

Zapata Computing, a leading quantum software company, provides tools for quantum-enhanced simulation and optimization, especially for problems with exponential complexity. Quantum techniques are well-suited to tackle:

• Multi-echelon network optimization

• Probabilistic scenario modeling

• Inventory control under demand uncertainty

• Rerouting options involving constrained border transitions

Accenture brings domain expertise in digital twin modeling and AI-driven logistics management, enabling integration of quantum models with enterprise platforms.

The Quantum Modeling Framework

The joint team developed a multi-layer framework for North American supply chain simulation using Orquestra®, Zapata’s quantum workflow platform. Core modules included:

1. Disruption Propagation Simulations

Using quantum-enhanced Markov models and graph analytics, the team simulated disruptions starting at specific border ports (e.g., Laredo, Windsor) and modeled their cascading effects on:

• Distribution centers

• Retail replenishment

• Just-in-time manufacturing lines

2. Cross-Border Routing Optimization

They formulated hybrid quantum-classical optimization problems to select optimal transshipment points, factoring in:

• Real-time customs clearance time data

• Truck fleet availability

• Fuel cost variability and carbon pricing

3. Resilience Scenario Planning

The team ran Monte Carlo-style simulations across various border closure and labor disruption scenarios, using quantum-inspired techniques to:

• Identify bottleneck nodes

• Recommend inventory pre-positioning

• Evaluate supplier redundancy strategies

Pilot Results: Insights into North American Network Stress Points

The initial modeling focused on automotive and electronics supply chains between the U.S. and Mexico. Findings revealed:

• Laredo, Texas is a critical node; minor delays create wide downstream effects

• Pre-positioning inventories in El Paso reduced network-wide delivery delays by 14%

• Hybrid quantum routing outperformed classical solvers in solution time by 25% for complex multi-constraint networks

Strategic Alignment with Industry Trends

This initiative reflects broader trends in quantum logistics research:

• Ford and 1QBit (2020) worked on parts delivery optimization

• Verizon and AWS Braket explored logistics network simulations

• Cambridge Quantum and Thales piloted quantum network resilience models

As geopolitical and climate risks intensify, logistics resilience modeling is moving from a niche capability to a strategic necessity for multinationals.

Integration Roadmap

The Accenture-Zapata project plans to expand in 2022 to:

• Incorporate rail and port disruption data

• Extend models to food and pharma supply chains

• Integrate quantum scenarios into SAP and Oracle ERP modules

A beta version of the quantum disruption dashboard is under trial with two Fortune 100 logistics clients.

Policy Context: USMCA and Quantum Innovation

The project aligns with government-led technology goals under:

• USMCA digital trade provisions encouraging advanced analytics

• National Quantum Initiative Act (U.S.) promoting applied quantum R&D

• Canada’s Pan-Canadian Quantum Strategy supporting commercial use cases

These frameworks provide funding and regulatory support for enterprise quantum experimentation in critical infrastructure sectors.

Challenges and Considerations

The partnership faced several challenges:

• Data integration across borders: Standardizing logistics and customs datasets remains difficult

• Solver interpretability: Translating quantum outputs into actionable insights for logistics managers

• Skill gaps: Bridging quantum model developers with supply chain strategists

Accenture initiated quantum literacy programs for logistics consultants and developed visual dashboards to make quantum output interpretable by operations teams.

Conclusion: Quantum Resilience for a Fractured World

The Accenture-Zapata collaboration demonstrates the strategic value of quantum tools in fortifying logistics networks. As North American trade flows face mounting volatility, quantum-enhanced simulation offers a powerful tool to anticipate, model, and mitigate disruptions.

With scalable use cases and demonstrated early value, the fusion of quantum modeling with enterprise supply chain strategy may define the next frontier in resilient logistics design.

Singapore’s Port Logistics at a Quantum Crossroads

As a global maritime hub, Singapore handles over 37 million TEUs (twenty-foot equivalent units) annually. PSA’s flagship terminals at Pasir Panjang and Tuas face unprecedented pressure from rising cargo volumes, pandemic-related backlogs, and growing vessel size. Traditional scheduling tools—based on rule-based heuristics or linear programming—struggle to resolve complex interdependencies between berth allocation, yard operations, and crane utilization.

This context made PSA an ideal candidate to explore quantum-enhanced logistics, particularly in areas where classical methods hit computational walls. In November 2021, PSA partnered with IBM Quantum to assess the potential of quantum algorithms in boosting port scheduling efficiency.

IBM Quantum and the Port Simulation Engine

The trial involved building a digital twin of PSA’s container terminal operations, integrating it with IBM’s Qiskit optimization stack and quantum backends via the IBM Quantum cloud.

The goal: translate container scheduling problems—typically formulated as job-shop scheduling or Quadratic Unconstrained Binary Optimization (QUBO) models—into forms executable by quantum and hybrid solvers.

The testbed included:

• Crane scheduling tasks with constraints for crane interference, work zones, and safety margins

• Container stacking decisions to minimize reshuffling while optimizing pickup windows

• Vessel berthing slot assignments under uncertain arrival time forecasts

Technical Stack and Solver Strategy

IBM and PSA engineers used hybrid algorithms combining quantum approximate optimization (QAOA) and classical pruning heuristics to tackle combinatorially intense segments of the problem space.

The computational flow involved:

1. Encoding scheduling problems into QUBO format using Qiskit Optimization

2. Running QAOA trials on IBM’s 7-qubit Falcon processor and classical simulators

3. Benchmarking against PSA’s traditional solvers and mixed-integer programming routines

Pilot Findings and Preliminary Results

While the trial remained exploratory in 2021, early simulations offered insights:

• Quantum-enhanced solvers matched classical models in slot assignment accuracy

• In certain crane dispatching subproblems, QAOA achieved 5–10% faster convergence

• Potential for better handling of dynamic rescheduling during weather disruptions

Engineers emphasized that while quantum advantage wasn’t yet achieved, hybrid quantum-classical workflows showed promise in improving responsiveness during peak port load scenarios.

Strategic Implications for PSA and Southeast Asia

The collaboration with IBM is part of PSA’s broader digitalization roadmap, which includes:

• PortNet 2.0: A smart port operating system with AI and IoT features

• Automated guided vehicles (AGVs) and smart cranes already operating at Tuas Mega Port

• Green logistics initiatives to optimize energy use in cargo handling

Quantum computing is seen as a longer-term bet—enabling port planners to cope with rising system complexity, volatile global demand, and tighter environmental constraints.

PSA executives noted that even partial gains in scheduling—on the order of 3–7%—could translate into millions in annual savings and faster turnaround for vessels.

IBM’s Broader Maritime Quantum Strategy

IBM’s partnership with PSA aligns with its efforts to embed quantum readiness in real-world industrial verticals:

• It has run port simulation pilots with Maersk and Port of Rotterdam

• Supported marine weather routing models with hybrid quantum solvers

• Worked with Singapore’s National Research Foundation (NRF) to integrate quantum pilots with national infrastructure programs

This strategy places maritime logistics alongside finance, pharmaceuticals, and energy as key sectors targeted for early quantum adoption.

Regulatory and Ecosystem Support

The PSA–IBM quantum trial received support from:

• Singapore Maritime and Port Authority (MPA) under its digital innovation blueprint

• SGInnovate, which promotes quantum tech entrepreneurship in Southeast Asia

• IBM’s Q Network, giving PSA access to hardware roadmaps and early developer tools

Together, these frameworks create a strong foundation for transitioning from pilot to production over the next five years.

Challenges and Opportunities

The trial revealed critical gaps to be addressed before operational deployment:

• Scalability: Today’s quantum hardware cannot yet handle the full complexity of port-wide scheduling

• Integration: Bridging quantum solvers with real-time data systems remains non-trivial

• Skill gaps: Training port planners and logistics analysts in quantum workflows is a work in progress

Yet PSA and IBM remain committed to building the scaffolding for long-term gains.

Outlook: A Quantum Future for Smart Ports

November 2021 may be remembered as the starting point for Southeast Asia’s entry into quantum logistics experimentation. With PSA International as a trailblazer, quantum optimization in container scheduling is moving from theoretical potential to pilot-tested feasibility.

As quantum hardware scales and hybrid methods mature, smart port operators around the world will look to Singapore’s quantum blueprint as a guide for transforming container logistics into a next-generation computational frontier.

ColdQuanta and SavantX: Quantum Meets AI in Logistics

ColdQuanta, a leader in quantum matter systems, has expanded its footprint in logistics through a deepening collaboration with SavantX. While ColdQuanta specializes in quantum sensing and computing platforms, SavantX focuses on AI-driven logistics platforms designed to handle complex scheduling, routing, and asset allocation.

Their initial collaboration at the Port of Los Angeles in 2020–2021 delivered a 60% increase in crane efficiency through an optimization platform called HONE (Hyper Optimized Nodal Efficiency). The November 2021 announcement expands the HONE platform into rail logistics, incorporating ColdQuanta’s quantum computing backend to boost optimization.

Why Rail Freight? A High-Impact Use Case for Quantum

The North American rail network handles over 28% of total freight ton-miles, but suffers from:

• Inefficient railcar routing

• Empty car repositioning challenges

• Complex intermodal transfer schedules

• Crew scheduling constraints

Rail systems are inherently networked and combinatorial, making them excellent candidates for quantum computing, which excels at solving optimization problems with large state spaces.

Key Quantum Use Cases in Rail:

• Dynamic train routing based on real-time congestion

• Railcar utilization optimization to reduce empty runs

• Crew and maintenance scheduling under strict regulatory constraints

• Rail yard sequencing for intermodal efficiency

HONE 2.0: Quantum-Enhanced Rail Optimization

The upgraded HONE 2.0 platform will feature a hybrid quantum-classical architecture:

• Classical AI layers for forecasting, data ingestion, and heuristics

• Quantum optimization engines (using ColdQuanta’s Hilbert quantum platform) for solving complex constraint-laden problems

The system will be deployed initially in pilot projects involving Class I railroads in the western U.S., with modules tailored for:

• Intermodal terminals in Southern California

• Bulk cargo routing hubs in the Midwest

• Yard management at high-traffic sorting facilities

Pilot Performance Metrics and Goals

The partners outlined the following expected improvements:

• 10–15% reduction in average train dwell time

• 12% increase in yard throughput under congested conditions

• Reduced crew scheduling conflicts and overtime

• Fewer empty car repositioning trips, reducing emissions and costs

Early simulations conducted in Q4 2021 showed promising results, with the quantum solvers outperforming legacy optimization tools in constraint satisfaction and scenario flexibility.

ColdQuanta’s Quantum Stack: Hardware + Software

ColdQuanta's value lies in its full-stack quantum approach:

• Hilbert Quantum Platform for gate-based quantum computing

• Albert cold atom sensors for precision tracking (being evaluated for railcar integrity monitoring)

• Quantum RF systems for secure data transmission

While most of HONE 2.0 will rely on quantum emulators initially, ColdQuanta plans to transition live modules to quantum processors as hardware maturity improves.

SavantX and Quantum-Augmented AI

SavantX’s logistics platform includes:

• High-performance AI-based freight optimization algorithms

• Real-time data integration pipelines from IoT and rail sensors

• Interactive dashboard visualizations for dispatchers and yard managers

By combining this with quantum backends, SavantX aims to break through bottlenecks that classical systems struggle with, particularly in high-traffic scenarios with large constraint matrices.

Strategic Implications and Broader Industry Impact

This partnership reflects growing interest in applying quantum computing to national-scale logistics. As supply chains become more dynamic and constrained, optimization becomes a key competitive advantage.

Industry Relevance:

• Class I railroads seek to digitize infrastructure without disrupting core operations

• Quantum tools offer enhanced resilience during peak seasons and disruptions

• AI-quantum synergies promise scalable, low-latency decisions

Future Outlook

The ColdQuanta–SavantX partnership aims to expand HONE 2.0 across:

• Canadian and Mexican cross-border freight networks

• Real-time fuel efficiency optimization for rail engines

• Quantum-enhanced network design, including simulation of rail infrastructure investments

By late 2022, the team hopes to demonstrate the first quantum-native rail scheduling module operating alongside traditional logistics software.

Conclusion: From Ports to Rails, Quantum Optimization Scales Up

The ColdQuanta and SavantX collaboration shows that quantum-powered logistics is not limited to maritime applications. With the launch of HONE 2.0 in U.S. rail networks, a new era of intelligent, responsive, and sustainable rail freight is on the horizon.

As quantum computing becomes more accessible, the logistics sector stands poised to benefit from unprecedented gains in efficiency, resilience, and system-wide coordination.

Singapore as a Quantum Logistics Testbed

Singapore’s port is one of the busiest and most technologically advanced in the world. As global supply chains were reeling from pandemic-related congestion in 2021, PSA International sought new computational tools to address rising throughput complexity, variability, and real-time disruption.

Recognizing the combinatorial nature of port operations—especially in berth allocation and container shuffling—PSA partnered with IBM Quantum to evaluate whether quantum-enhanced solvers could offer breakthroughs beyond classical optimization methods.

This made PSA one of the first major port operators to publicly pursue quantum computing as a long-term solution to logistics challenges.

Project Goals: Managing Uncertainty at the Quay

The PSA–IBM collaboration focused on three core problems:

1. Berth allocation optimization under unpredictable vessel arrival times.

2. Container yard reshuffling to reduce re-handling costs and crane idling.

3. Disruption recovery strategies after delays, equipment failure, or bad weather.

The goal was to improve performance in scenarios with millions of variables, limited planning windows, and real-time data constraints. These are precisely the kinds of problems quantum computers—especially hybrid systems—are expected to address in the coming decade.

IBM Quantum Systems in Use

The pilot utilized IBM’s gate-based quantum computing systems available through the IBM Quantum Network, particularly its:

• 27-qubit Falcon processors for early-stage modeling.

• Qiskit SDK for algorithm development.

• Cloud-based access via the IBM Quantum Experience platform.

The team experimented with quantum-inspired and quantum-native algorithms, depending on problem size and solver maturity.

Use Case 1: Quantum-Aware Berth Scheduling

Berth scheduling involves aligning ship arrivals with limited dock space, accounting for:

• Arrival time uncertainty

• Ship size and crane requirements

• Loading/unloading durations

• Tidal and weather influences

The team developed a quantum-enhanced constraint satisfaction model that mapped these variables to a qubit register.

Using Variational Quantum Eigensolver (VQE) techniques and hybrid preprocessing, IBM’s solvers explored berth-slot permutations that minimized vessel waiting time while balancing crane utilization.

Initial simulations on IBM’s 27-qubit system demonstrated:

• Higher solution diversity for last-minute vessel bunching

• Smoother allocation outcomes under arrival-time variability

• A 6–9% reduction in average berth waiting times in small-scale trials

Use Case 2: Container Yard Optimization

In large ports like Singapore’s Tuas or Pasir Panjang terminals, containers are stacked in dense yards where reshuffling is costly and time-consuming.

PSA tested Quantum Approximate Optimization Algorithm (QAOA) formulations to:

• Minimize rehandling during container retrieval

• Predict optimal yard stacking layouts based on ship schedules

• Improve yard crane task assignments

The quantum solver outperformed classical heuristics in some constrained layouts, particularly when:

• Delivery windows were tight

• Stacks had uneven height constraints

• Yard zones had varied reachability

Though the quantum system could only simulate limited container sets, the findings highlighted how quantum heuristics may enhance layout planning, especially as hardware scales.

Use Case 3: Reactive Scheduling After Disruptions

Ports frequently face unexpected delays—e.g., from crane failures or customs issues. PSA and IBM modeled how quantum systems might aid disruption response by rapidly reassigning berths and crane jobs.

The goal was to reduce total downtime across the yard by recalculating all impacted resource allocations within seconds.

The hybrid quantum-classical pipeline used:

• Real-time disruption inputs

• A decision tree encoded into a quantum circuit

• Classical post-processing for feasibility validation

Though still in early phases, the approach showed potential in reducing cascading delays—by generating faster, more balanced recovery plans than existing rule-based systems.

Strategic Objectives and Ecosystem Fit

The PSA–IBM project aligned with several broader strategic objectives:

• Operational resilience: Building tools for rapid decision-making under uncertainty.

• Digital leadership: Reinforcing Singapore’s status as a smart port innovation leader.

• Quantum capacity-building: Training logistics engineers in quantum modeling techniques.

Singapore’s national initiatives such as the Quantum Engineering Programme (QEP) and Port 4.0 Transformation Roadmap actively supported such experiments as part of a long-term digital trade strategy.

Global Context: Quantum Port Logistics Takes Shape

The PSA–IBM announcement marked a significant point in the emergence of quantum logistics in port operations. Globally:

• Port of Los Angeles began collaborating with USC on quantum scheduling models.

• Rotterdam’s Digital Twin Program explored quantum algorithms for container flow forecasting.

• Hamburg Port Authority initiated quantum research with DLR and Siemens in late 2021.

Together, these initiatives signaled the formation of a quantum-ready port ecosystem, where early experimentation is laying the groundwork for the 2030s.

Challenges Identified

While promising, PSA and IBM acknowledged several current limitations:

• Quantum volume was too small for full-port-scale problems.

• Data integration from IoT, TMS, and ERP systems needed preprocessing pipelines.

• Talent readiness: Bridging port operations and quantum programming required new cross-disciplinary roles.

IBM began working with NUS and NTU to develop quantum training modules for supply chain professionals, aiming to close the skills gap over time.

Toward Port Optimization-as-a-Service

IBM’s roadmap included offering port-specific quantum optimization modules via its cloud-based IBM Quantum Services, enabling:

• Simulation-based planning for shipping alliances

• Real-time decision-support dashboards for quay operators

• APIs that port logistics software vendors can integrate with

PSA expressed interest in developing a modular quantum layer within its internal optimization stack—allowing planners to test quantum-generated options alongside classical models.

Environmental and Economic Impacts

The pilot also linked to sustainability metrics:

• Improved berth scheduling reduces fuel usage from idling vessels

• Better yard layout planning can lower crane energy consumption

• Faster disruption response minimizes demurrage and late penalties

Even small gains from quantum planning can have compound environmental benefits across thousands of daily container movements.

Conclusion

The October 2021 collaboration between PSA International and IBM Quantum marked a major step in integrating quantum computing into port logistics. Focused on berth scheduling, yard operations, and disruption management, the pilot demonstrated how early-stage quantum algorithms can complement classical systems in managing uncertainty and complexity.

As hardware scales and hybrid solvers evolve, port operators like PSA are laying the foundations for quantum-enhanced global trade infrastructure, positioning themselves ahead in the digitalization of logistics.

Building Quantum-Aware Supply Chains

DHL has long emphasized digital innovation, from warehouse automation to predictive analytics. In 2021, the logistics giant deepened its future-tech play by piloting quantum computing use cases in collaboration with Honeywell Quantum Solutions. Their joint goal: determine how quantum algorithms could enhance multi-modal cargo efficiency across air, sea, and port logistics.

The initiative was focused on three key areas:

• Cargo slot allocation and loading sequencing

• Cross-modal route optimization

• Real-time disruption mitigation through predictive modeling

This marked a notable shift from theoretical studies to direct application testing using Honeywell’s advanced trapped-ion quantum hardware—at the time among the highest fidelity commercial quantum systems.

The Quantum Hardware at the Core

Honeywell Quantum Solutions’ processor, the H1 trapped-ion quantum computer, served as the backbone of the experiment. Key technical advantages included:

• Mid-circuit measurement, allowing decision branches within computations.

• All-to-all connectivity, improving qubit interaction efficiency.

• High gate fidelity above 99.9%, which allowed more reliable algorithm execution.

DHL’s logistics problems, highly combinatorial in nature, were ideal for early-stage quantum optimization testing—especially for modeling allocation constraints in shipping containers and air cargo pallets.

Use Case 1: Cargo Load Optimization

The first use case modeled a simplified version of DHL's cargo loading workflows:

• Containerized freight had to be packed within dimensional and weight constraints.

• Regulatory and destination rules created additional hard constraints.

• Optimization goals included minimizing empty volume and maximizing delivery timeliness.

Using quantum approximate optimization algorithms (QAOA), the team encoded cargo combinations and constraints into qubit states. Early-stage runs on Honeywell's H1 processor showed potential benefits in:

• Reducing computational time for constrained packing problems.

• Enabling flexible load configurations in response to late shipment changes.

While classical solvers remain more scalable today, the quantum approach delivered novel solution paths that helped DHL rethink its heuristic models.

Use Case 2: Air-Sea Intermodal Coordination

The second use case addressed route optimization across multimodal journeys—e.g., moving cargo from air hubs to sea ports with tight handoff windows.

DHL modeled a regional network involving:

• European cargo flights arriving at major airports (e.g., Frankfurt, Heathrow).

• Cargo transfers to sea-bound shipments via truck corridors.

• Port departure timelines and loading capacities.

The problem was mapped onto a quantum-inspired graph traversal model with resource constraints (vehicle capacity, distance, time windows).

Running hybrid solvers—combining Honeywell’s quantum processor with classical optimization layers—the system tested several intermodal handoff scenarios. Results demonstrated:

• Better adherence to tight deadlines.

• Reduced late departures due to smarter truck-port alignment.

• A foundation for co-optimizing freight timing across transportation modes.

Use Case 3: Disruption Simulation and Response

In a third experiment, DHL simulated a cargo disruption scenario (e.g., a missed connection at an intermediate hub). Quantum-enhanced solvers were used to:

• Reconfigure cargo plans within seconds of the disruption.

• Explore thousands of alternative configurations for re-routing.

• Prioritize critical cargo items based on urgency and perishability.

While current quantum machines are not fast enough for live, global-scale replanning, the prototype showed that quantum techniques can rapidly suggest high-quality alternatives in complex, high-constraint systems—potentially acting as decision-support modules.

Strategic Rationale and Long-Term Vision

DHL Supply Chain emphasized that while near-term quantum supremacy in logistics is years away, the early involvement helps:

• Build internal quantum literacy among operations planners.

• Test integration methods with existing transport management systems (TMS).

• Develop a library of quantum-friendly logistics use cases.

Honeywell (now part of Quantinuum) saw the partnership as proof that real-world logistics firms are ready to experiment with quantum hardware. The goal is not replacing classical systems outright, but enhancing combinatorial modeling capabilities.

Industry Impact and Quantinuum’s Evolution

Honeywell’s quantum division would soon merge with Cambridge Quantum to form Quantinuum, one of the largest integrated quantum computing firms by late 2021. The DHL pilot became part of Quantinuum’s logistics and supply chain research showcase.

This reflected a broader logistics trend, as companies began formalizing quantum innovation pathways:

• UPS and Zapata Computing launched a quantum route planning initiative.

• FedEx began exploring quantum cryptography for package security.

• DB Schenker partnered with Fraunhofer to study quantum forecasting.

The DHL-Honeywell project stood out as one of the first to test gate-based quantum processors on real-world freight problems, rather than relying solely on emulators or annealing platforms.

Technical Lessons and Constraints

Key takeaways from the October 2021 experiments included:

• QAOA tuning is non-trivial: Performance was highly sensitive to parameter settings.

• Hybrid integration works best: Classical pre-processing helped reduce quantum circuit depth.

• Scalability is still a challenge: Models were limited to small cargo batches and regional networks due to hardware limits.

Still, DHL and Quantinuum agreed that with scaling advances, these methods could handle full-fleet optimizations in future logistics planning.

Toward Quantum-Enhanced Logistics Platforms

Looking forward, DHL planned to:

• Create a quantum sandbox for internal logistics experiments.

• Partner with Quantinuum to build domain-specific applications, including cargo delay risk scoring and slot allocation tools.

• Publish its results in open-access quantum-logistics journals to help shape industry standards.

Quantinuum, meanwhile, began offering its InQuanto and TKET platforms to logistics clients, enabling broader experimentation beyond DHL.

Policy and Market Alignment

This quantum cargo research aligned with EU and global efforts to modernize supply chains:

• The EU’s Digital Compass roadmap promotes quantum and AI investments in transport.

• Germany’s Federal Ministry of Digital and Transport began exploring next-gen routing for air and freight mobility hubs.

• The World Economic Forum identified quantum logistics as a priority use case for sustainable freight networks.

Challenges and Opportunities

Despite promise, several hurdles remained:

• Hardware limitations: Qubits and coherence times still limit complexity.

• Workflow integration: Embedding quantum tools into existing DHL systems required custom interfaces.

• Skill gaps: Both DHL and Quantinuum faced challenges in recruiting quantum-savvy supply chain engineers.

But the upside is clear: quantum modeling may offer new paths to optimization in a sector increasingly pressed by global delays, emissions targets, and cost volatility.

Conclusion

The Honeywell-DHL quantum logistics pilot in October 2021 was a milestone in translating quantum theory into applied cargo operations. By tackling real freight problems with early gate-based processors, the project validated quantum’s long-term potential in global logistics.

Though still in the experimental phase, such pilots help de-risk future deployments and establish quantum-aware planning as part of logistics digitalization. DHL’s leadership may inspire broader adoption across the freight sector—especially as quantum hardware and hybrid software continue to evolve.

Why Cold Chain Logistics Needs Quantum Solutions

Cold chain logistics deals with the transportation and storage of temperature-sensitive goods. Any deviation from required temperature ranges can lead to product spoilage, especially for vaccines, biologics, dairy, meat, and seafood. Traditional optimization systems struggle with the dynamic constraints posed by:

• Varying temperature requirements across different product categories

• Time-critical delivery windows for maintaining efficacy

• Route disruptions due to weather, traffic, or customs delays

Mitsubishi's logistics division, handling perishable goods across Asia and North America, identified these limitations and sought a more adaptive, constraint-sensitive routing engine.

The Quantum Approach: D-Wave's Hybrid Solver Platform

D-Wave brought to the table its Leap hybrid solver, which blends classical computing with quantum annealing to solve large-scale optimization problems.

Key features include:

• Constraint-based optimization, allowing for cold chain-specific parameters like thermal tolerance levels

• Multi-objective route planning, balancing delivery time, cost, and refrigeration stability

• Dynamic recomputation, triggered by real-time sensor inputs from reefer containers and IoT trackers

Mitsubishi integrated D-Wave's APIs into its cold chain management system to simulate delivery routes for vaccines and fresh produce under various environmental stress conditions.

Pilot Program Overview

In Q4 2021, the partners launched a three-month pilot covering:

• Tokyo to Osaka pharmaceutical deliveries involving biologic samples

• Refrigerated seafood exports from Hokkaido to Los Angeles

• Pan-Asia fresh produce logistics, focusing on last-mile delivery within Bangkok and Manila

Metrics Tracked:

• Temperature deviation incidents (logged via IoT sensors)

• Fuel consumption across reefer fleets

• Delivery time adherence

• Product spoilage rates

Early Results: Efficiency and Safety Gains

Initial findings from the pilot showed:

• 22% reduction in spoilage incidents in biologic deliveries

• 11% decrease in fuel use due to optimized route-temperature correlation

• Improved delivery time consistency by 17%, especially in urban last-mile zones

These improvements were credited to the hybrid solver's ability to pre-calculate backup routing paths and dynamically reallocate reefer trucks based on weather and traffic inputs.

Strategic Goals and Regional Relevance

The Mitsubishi–D-Wave collaboration is seen as a strategic move to:

• Strengthen Japan's vaccine distribution resilience in a post-pandemic context

• Support ASEAN food security initiatives through better produce distribution

• Reduce cold chain logistics emissions, aligning with Japan’s Green Growth Strategy

Cold chain systems, traditionally seen as cost centers, are now being reimagined as tech-driven, value-adding services thanks to optimization innovation.

Technology Integration: How It Works

The solution stack comprises:

1. IoT and telematics sensors recording reefer container temperature, location, and vibration

2. Data ingestion layer that feeds environmental data to D-Wave’s Leap system

3. Hybrid quantum solvers modeling delivery as a constraint optimization problem

4. Dispatch decision engines integrated into Mitsubishi’s logistics control tower interface

This setup allows cold chain managers to visualize multiple routing outcomes based on forecast disruptions and make more informed decisions.

Broader Industry Implications

The pilot has drawn attention across logistics sectors:

• Pharmaceutical firms are evaluating D-Wave’s tech for global vaccine routing

• Seafood exporters are eyeing quantum solvers for minimizing shelf-life loss

• Third-party logistics providers (3PLs) are exploring plug-in integration with existing TMS platforms

With an estimated $750 billion annual global market for cold chain logistics, even marginal efficiency gains have large financial and environmental impacts.

Challenges and Lessons Learned

Despite success, the project highlighted some key considerations:

• Solver tuning requires extensive domain data to accurately simulate spoilage thresholds

• IoT data latency can limit the real-time responsiveness of route recalculations

• Quantum algorithm expertise remains scarce within logistics organizations

To address this, Mitsubishi is investing in quantum upskilling programs for its supply chain engineers and is working with Japanese universities on joint R&D projects.

Roadmap for 2022 and Beyond

Following the pilot, Mitsubishi and D-Wave plan to:

• Expand quantum optimization to marine cold chain networks

• Integrate with blockchain for end-to-end cold chain traceability

• Extend hybrid solver use to warehouse thermal zone optimization

Japan’s Ministry of Economy, Trade and Industry (METI) has expressed support, citing this as a benchmark case for next-generation supply chain resilience.

Conclusion: Cold Chains Go Quantum

The October 2021 Mitsubishi–D-Wave initiative proves that quantum optimization is not limited to theoretical models. In high-stakes environments like cold chains, where minutes and degrees matter, hybrid quantum approaches deliver measurable value. As infrastructure matures, expect quantum-enabled logistics to become a core component of how temperature-sensitive goods are delivered worldwide.

Rotterdam: A Global Maritime Logistics Hub Ready for Quantum

Handling over 14 million TEUs (twenty-foot equivalent units) annually, the Port of Rotterdam manages immense volumes of cargo under tight schedules. The complexity of container arrival, inspection, storage, and dispatch presents ideal conditions for quantum algorithms to deliver value.

Challenges include:

• Yard slot optimization for varying container sizes and turnover rates

• Predictive berth scheduling under unpredictable traffic and weather

• Crane and equipment dispatching across dynamic zones

• Congestion forecasting from multimodal inland transport

To address these, the port partnered with quantum software startup QC Ware and leveraged IBM’s Qiskit Runtime on real quantum hardware and emulators.

Objectives and Structure of the Rotterdam Quantum Pilot

The project was initiated as part of the Digital Twin Rotterdam initiative—a smart port program that uses AI, sensors, and simulation to digitize port operations. Quantum computing added a new dimension of computation for tackling NP-hard optimization problems in:

Primary goals:

• Minimizing container dwell time using quantum-based bin-packing solvers

• Optimizing gate and yard traffic flow via hybrid quantum routing models

• Improving accuracy in ETA (estimated time of arrival) prediction with quantum-enhanced regression models

The pilot focused on the Euromax and Maasvlakte terminals, two of the port’s most complex container zones.

QC Ware's Hybrid Quantum Algorithms in Action

QC Ware adapted its Forge platform to run logistics-specific quantum routines, including:

• Quantum Approximate Optimization Algorithms (QAOA) for crane assignment

• Variational Quantum Eigensolvers (VQE) for resource scheduling

• Classical-quantum co-processing for Monte Carlo simulations on container movement probabilities

These were compared with classical heuristics and deployed in digital twin simulations using historical and real-time data.

Performance indicators:

• 12% improvement in container placement efficiency

• 9% reduction in truck turnaround time within the terminal gates

• More stable congestion forecasting curves during peak unloading periods

IBM Quantum’s Role and Technical Enablement

IBM provided access to its quantum cloud services, including:

• Qiskit Runtime for efficient hybrid execution

• Simulated runs on 127-qubit IBM Eagle processors

• Integration with IBM’s AI Ops and weather prediction APIs for multimodal optimization

These integrations allowed the port’s analytics team to experiment with quantum scheduling under real-world constraints.

Strategic Alignment and European Leadership in Quantum Logistics

The project aligns with the Dutch government’s National Growth Fund, which supports quantum innovation under the Quantum Delta NL program.

Rotterdam aims to be a smart port benchmark—demonstrating:

• EU data sovereignty in maritime tech

• Carbon and time savings via better resource scheduling

• Scalability of quantum models across other EU ports (Antwerp, Hamburg, Le Havre)

Outlook: Toward Full-Scale Quantum Deployment

Based on early success, Rotterdam plans to:

• Expand quantum optimization to rail and barge scheduling

• Feed quantum predictions into automated crane systems

• Build in-house quantum talent through academic partnerships

The goal is to evolve from simulation to live operational integration by 2024, as hardware matures and software stabilizes.

Conclusion: Rotterdam Sets the Quantum Course for Maritime Logistics

This pilot marks a significant turning point in quantum logistics, proving that hybrid quantum algorithms can address real-world problems today.

As QC Ware, IBM, and the Port of Rotterdam iterate on this model, other global ports are closely watching. The race for quantum operational advantage in maritime logistics has officially begun.

Quantum Algorithms Take Flight in Global Cargo

Air cargo logistics is a cornerstone of high-speed global trade. Yet its optimization remains challenging due to complex airspace regulations, changing weather conditions, weight constraints, time-sensitive cargo, and environmental considerations.

In September 2021, Airbus and QC Ware announced the completion of a six-month pilot under Airbus's Quantum Technology Initiative. The project’s central goal: explore how quantum algorithms could help solve air cargo routing problems that are notoriously difficult for classical systems, especially during peak congestion or emergencies.

Problem Scope: NP-Hard Meets Real World

Air cargo routing involves solving variations of the Vehicle Routing Problem (VRP) and the Travelling Salesman Problem (TSP)—both NP-hard. When additional constraints are introduced, such as:

• Airport slot restrictions

• Aircraft-specific load balancing

• Hazmat regulations

• Cross-modal connections (e.g., air-to-road logistics)

…the solution space grows exponentially.

Airbus and QC Ware aimed to encode these problems into quantum-amenable formats and explore whether quantum algorithms—specifically quantum approximate optimization algorithms (QAOA) and variational quantum circuits—could find better solutions faster than classical heuristics.

Use Case: Europe-to-Asia Multi-Stop Cargo Routing

The pilot project focused on a real-world simulation of Airbus Cargo operations involving:

• A wide-body freighter departing from Frankfurt

• Cargo deliveries at multiple Asian hubs (e.g., Dubai, Mumbai, Singapore)

• Backhaul routing for aircraft efficiency

• Coordinated truck handoffs at hub destinations

The objective was to find an optimal route balancing fuel burn, carbon emissions, delivery timing, and airport slot availability—all under dynamic real-time conditions.

QC Ware’s Role and Quantum Workflow

QC Ware, a Palo Alto and Paris-based quantum software firm, contributed expertise in hybrid algorithms and quantum-classical co-processing. Their approach included:

1. Problem Encoding: Transforming the routing constraints into QUBO (Quadratic Unconstrained Binary Optimization) format.

2. Solver Deployment: Running the problems on simulators and early hardware (D-Wave Advantage and IonQ trapped ion devices).

3. Result Validation: Comparing outcomes with Airbus’s existing optimization stack built on mixed-integer linear programming (MILP).

Their internal toolkit, Forge, was used to model the air cargo routing scenario and translate it into quantum workflows.

Key Findings from the September 2021 Report

The feasibility study delivered several critical insights:

• Fuel efficiency gains of up to 4.2% in route planning during moderate traffic conditions compared to classical heuristics.

• Improved resilience in scheduling under disruptions (e.g., last-minute cargo adds, airport slot losses).

• Quantum-enhanced dynamic re-routing, which allowed the simulation to reoptimize in near-real time based on simulated weather disturbances.

While the algorithms didn't yet outperform classical solvers in all cases, they consistently found more flexible routing solutions, offering better trade-offs between speed, cost, and emissions.

Industry Implications: Toward Quantum-Aware Aviation

The Airbus-QC Ware pilot marks one of the first known attempts to apply quantum computing to commercial aviation logistics. As part of the broader Quantum for Aviation Logistics (Q4AL) initiative under Airbus’s innovation strategy, the pilot reflects a multi-decade vision where:

• Quantum processors assist flight dispatchers and cargo planners

• Real-time scheduling integrates quantum routing with AI weather forecasts

• Carbon-efficient flight plans are co-optimized with regulatory and delivery constraints

This approach may significantly benefit freight-dense aviation sectors, such as:

• Medical supply airlifts

• Cross-border e-commerce shipments

• Cold chain cargo requiring precise delivery timing

Integration Challenges and Lessons Learned

The study also revealed multiple technical and operational challenges:

• Quantum noise: Hardware fidelity remains a limiting factor, especially for large QUBOs.

• Result interpretability: Some quantum outputs required significant post-processing to be actionable.

• Latency: While solvers showed strong theoretical potential, end-to-end integration into Airbus’s live systems needs streamlining.

To address this, Airbus plans to invest in a quantum middleware layer—capable of translating between logistics objectives and evolving quantum architectures.

Strategic Partnerships and Ecosystem Building

The September 2021 report coincided with broader moves in quantum aviation:

• Airbus's collaboration with Pasqal on quantum simulation for aerodynamics

• Lufthansa Industry Solutions exploring quantum-safe communication for flight dispatch

• Singapore Airlines’ cargo division joining Asia Quantum Consortium for joint research in logistics quantumization

Meanwhile, QC Ware continues working with other transport firms, including FedEx and DB Schenker, to generalize its algorithms across multiple cargo domains.

Next Steps: Scaling Beyond Simulation

With simulation success achieved, Airbus’s quantum team announced plans to:

• Conduct small-scale live trials on intra-European cargo routes by 2023

• Partner with national aviation regulators to ensure quantum-assisted routing meets compliance standards

• Invest in quantum-augmented digital twins of entire cargo routes, from warehouse to final delivery

Airbus’s long-term vision includes making quantum flight routing modules available as part of its Skywise platform, opening them to broader logistics ecosystems.

Conclusion: Air Cargo Joins the Quantum Logistics Frontier

As of September 2021, Airbus and QC Ware’s joint pilot provides a compelling look at how quantum computing may eventually transform air cargo logistics. While commercial deployment remains years away, their work shows how even today’s noisy quantum devices can bring measurable value in highly constrained, mission-critical operations.

For an industry defined by timing, complexity, and cost optimization, quantum technologies could soon become a powerful tool for future-ready cargo planning.

Ocado’s Quantum Ambitions in Retail Logistics

Ocado’s leadership in logistics automation is well established. With one of the world’s most advanced smart warehouse systems—complete with autonomous bots, AI prediction, and advanced robotics—Ocado has long pushed the boundary of what's possible in e-commerce fulfillment.

In September 2021, it took a bold step further, exploring the use of quantum computing for inventory flow modeling, particularly during high-variability demand conditions such as flash sales or seasonal peaks.

The initiative is part of a joint applied research program with Phasecraft, a UK quantum software company, and UCL’s Centre for Quantum Technologies, supported under the UKRI's National Quantum Technologies Programme.

The Logistics Challenge: Real-Time, Dynamic Inventory Decisions

Ocado’s fulfillment centers face a unique challenge: orchestrating thousands of robots that pick and move items in a grid-based storage structure, while responding to:

• Rapidly fluctuating customer demand

• Limited product shelf lives (e.g., fresh produce)

• Complex SKU-level inventory dependencies

Traditional optimization models can struggle under these conditions due to their combinatorial nature. Quantum computing offers potential relief by enabling faster, probabilistic solutions to problems involving millions of variables and constraints.

Phasecraft’s Role: Building Quantum-Ready Inventory Solvers

Founded by quantum physicists from UCL and the University of Bristol, Phasecraft specializes in quantum algorithms for near-term devices. Their approach for Ocado focused on:

• Translating warehouse inventory and robot picking tasks into quantum-appropriate models, such as QUBOs and Ising Hamiltonians.

• Applying variational quantum eigensolvers (VQEs) and quantum approximate optimization algorithms (QAOAs) for inventory pathfinding and stock redistribution.

Early experiments ran on quantum simulators and small-scale superconducting quantum processors provided by Rigetti and IBM Q.

Key Focus Areas in the 2021 Trials

1. Quantum-optimized stock placement: Minimizing robot travel time by calculating ideal stock distribution patterns using hybrid quantum solvers.

2. Out-of-stock mitigation: Forecasting demand spikes and recommending dynamic stock reallocations across Ocado’s warehouse network.

3. Order batching optimization: Using quantum-enhanced heuristics to cluster customer orders for more efficient picking and packing.

These functions were tested within a digital twin of an Ocado Smart Platform (OSP) fulfillment center.

Results from Initial September 2021 Trials

While early-stage, the simulations revealed notable performance improvements:

• 13–16% reduction in average robot path length during peak fulfillment times.

• Up to 11% improvement in pick accuracy under fluctuating demand conditions.

• Demonstrated scalability of hybrid quantum-classical models when integrated into Ocado’s real-time decision systems.

Crucially, Phasecraft’s models could be run partially on classical hardware, ensuring compatibility with current IT infrastructure while preparing for future quantum advantage.

Strategic Vision: Quantum as Part of the Fulfillment Stack

Ocado Technology outlined a long-term vision in which quantum computing augments AI, robotics, and digital twins in a unified logistics ecosystem.

Goals include:

• Reducing fulfillment latency to under 5 minutes for common order types.

• Increasing resilience in the supply chain through faster recalibration of stock levels.

• Developing quantum-enhanced simulations for warehouse design and layout planning.

Ocado sees quantum as complementary—not a replacement—for classical optimization or AI, instead focusing on areas where current methods hit performance ceilings.

Industry Implications: Quantum-Enhanced Fulfillment Networks

Ocado’s work stands out as a rare example of quantum application in retail logistics, a space typically dominated by traditional enterprise resource planning (ERP) systems.

Their move signals several industry trends:

• Retailers investing in quantum-readiness, not just for cybersecurity, but for operational advantage.

• Hybrid logistics architectures, where quantum and classical systems co-optimize real-time workflows.

• Growing need for quantum-literate supply chain engineers, able to translate logistics problems into quantum frameworks.

Other retail-adjacent moves around the same time include:

• Amazon Web Services (AWS) Braket collaborations with logistics optimization startups

• Walmart exploring post-quantum secure messaging in its logistics IoT systems

• Alibaba DAMO Academy piloting quantum route planning in urban e-commerce delivery zones

Policy and Academic Ecosystem Support

The project aligns with the UK government’s 10-year quantum strategy and draws on funding from:

• UK Research and Innovation (UKRI)

• Innovate UK Smart Grant

• EPSRC Centre for Doctoral Training in Delivering Quantum Technologies

It also reflects the growing strength of the London–Cambridge–Bristol corridor as a European quantum innovation zone.

Challenges Identified in 2021 Trials

Despite promising results, several challenges remain:

• Hardware constraints: Quantum processors are still limited in scale and coherence time.

• Algorithm tuning: Finding the right problem encoding is often harder than solving the problem itself.

• Data compatibility: Integrating quantum outputs into classical warehouse management systems required significant pre- and post-processing.

Ocado is addressing these by investing in internal talent development, open-source toolkits, and closer collaboration with the UK’s national quantum hubs.

Conclusion: Quantum Logistics Gets Retail-Ready

Ocado Technology’s September 2021 announcement marks a milestone in the evolution of warehouse logistics. By applying cutting-edge quantum algorithms to everyday inventory challenges, the company is demonstrating how near-term quantum advantage can be harnessed in practical, commercial settings.

As quantum hardware matures, projects like this may serve as a blueprint for other e-commerce and retail supply chains seeking smarter, faster, and more resilient fulfillment systems.

Rotterdam's Quantum Leap in Port Logistics

The Port of Rotterdam has long stood as a beacon of innovation in maritime logistics, known for early adoption of automation, AI, and digital twins. In September 2021, it took another pioneering step—exploring the integration of quantum computing and quantum communication into its smart logistics infrastructure.

The initiative is part of a broader program under the SmartPort Foundation, involving multiple partners across academia, logistics providers, and quantum hardware developers. The aim is clear: to demonstrate how quantum-enhanced digital twins—virtual replicas of container terminals and flows—can be used to make smarter, real-time logistics decisions.

Why Quantum in Port Logistics?

Container ports are high-complexity environments. Each vessel brings thousands of containers that must be sorted, routed, and loaded or unloaded in precise sequences, all while coordinating trucks, cranes, trains, and inland shipping.

Traditional digital twins already simulate these operations using classical computing. However, as container volume and operational complexity increase, classical simulations encounter combinatorial bottlenecks. Quantum computing offers a potential breakthrough.

Quantum Optimization Potential:

• Container yard slotting: Minimizing crane travel distances and container reshuffles using combinatorial quantum solvers.

• Berth planning: Solving multi-objective optimization for vessel scheduling and berth assignment.

• Traffic congestion management: Real-time prediction and rerouting for trucks using hybrid quantum models.

These use cases align well with quantum annealing, tensor networks, and variational algorithms, which are suitable for solving NP-hard optimization challenges common in port logistics.

The Quantum Digital Twin Architecture

Rotterdam’s prototype digital twin merges three key components:

1. Data integration layer: Pulls real-time data from container management systems (CMS), terminal operating systems (TOS), crane telemetry, weather feeds, and vessel arrival schedules.

2. Quantum optimization engine: Developed with support from TU Delft’s QuTech group, the engine translates key decision variables (e.g., container locations, truck arrival patterns, crane schedules) into QUBO (Quadratic Unconstrained Binary Optimization) models for quantum processing.

3. Quantum-secure communication overlay: Using quantum key distribution (QKD) via Q*Bird’s entangled photon protocols, the system ensures secure transmission of sensitive logistics and routing data between port nodes.

Project Objectives and Scope

Launched in Q3 2021, the initial goal of the project was to evaluate:

• Feasibility of quantum solvers in container planning.

• Latency and accuracy of hybrid digital twin responses during peak port operations.

• Cyber-resilience improvements using QKD for logistics communications.

• Simulation fidelity compared to traditional predictive tools.

The first testbed was implemented in a controlled simulation environment emulating the Euromax Terminal, one of the most automated sections of the port.

Early Results and Insights

By late September 2021, the Port of Rotterdam Authority and partners reported the following outcomes from their simulations:

• A 9–11% reduction in average crane travel distance, when using quantum-optimized container slotting versus traditional heuristics.

• Berth scheduling improvements that reduced vessel waiting times by up to 7% under congested conditions.

• Increased resilience against schedule perturbations due to the probabilistic nature of quantum solutions allowing for multiple near-optimal paths.

Perhaps most critically, the introduction of quantum-safe encryption protocols showed no significant latency penalty, opening doors for commercial deployment of QKD in a high-throughput, latency-sensitive logistics environment.

Partners Driving the Innovation

TU Delft and QuTech

The quantum optimization layer was spearheaded by researchers from TU Delft’s Faculty of Electrical Engineering, Mathematics, and Computer Science and its joint research institute QuTech, which focuses on scalable quantum computing infrastructure. Their role included:

• Translating logistics challenges into quantum representations.

• Developing hybrid algorithms to run on both quantum simulators and early quantum processors.

• Coordinating modeling workshops with port logistics engineers to tune objectives and constraints.

Q*Bird

A startup founded out of Delft, Q*Bird specializes in quantum communication systems using entangled photon pair distribution. Their QKD protocols formed the secure transport layer for logistics command signals across the port’s network.

The company also piloted an early implementation of quantum network nodes, anticipating a future port-wide quantum network infrastructure.

Broader Strategic Alignment

This initiative fits into larger national and European efforts:

• NL Quantum Delta: A €615 million Dutch initiative to advance quantum computing and communication, of which TU Delft and Q*Bird are members.

• EU Quantum Flagship: Encourages cross-industry pilots for quantum application in logistics and infrastructure.

• Rotterdam’s Port Vision 2030: Prioritizes digital twins, automation, and secure, scalable data infrastructure for growing global trade.

By aligning with these programs, the Port of Rotterdam ensures access to funding, research networks, and policy guidance for sustainable scaling.

Challenges and Remaining Questions

Despite promising results, the September 2021 report also flagged technical and operational challenges:

• Solver calibration: Quantum algorithms must be tightly tuned to avoid inefficient or unstable outputs in real-time simulations.

• Hardware maturity: While QUBO models were tested on simulators and early-stage quantum annealers, current quantum processors remain limited in scale.

• Workforce skills gap: Port engineers require upskilling to understand and trust quantum-enhanced system suggestions.

As part of the next phase, the Port plans to develop training modules and graphical interpretability layers to bridge the human-machine interface challenge.

Looking Ahead: Scaling Quantum in Maritime Logistics

The Port of Rotterdam aims to expand the quantum digital twin project through:

• Real-world pilot integration with select terminals by 2023.

• Exploring quantum co-processing with AI-powered predictive maintenance systems.

• Participation in a broader Quantum Internet testbed being developed in South Holland.

These steps mark Rotterdam’s intent to not only digitize, but quantize, its future logistics operations.

Conclusion: A Model for Global Ports

This September 2021 milestone showcases how quantum computing and communication can offer actionable gains in port logistics. The Port of Rotterdam, backed by academic and quantum tech expertise, has laid the groundwork for a new paradigm: quantum-enhanced infrastructure optimization.

If successful at scale, this model could influence quantum logistics adoption in other major ports—Singapore, Los Angeles, Shanghai—positioning Europe at the forefront of quantum transformation in global trade.

Digital Twins Meet Quantum Algorithms

Digital twins have become a cornerstone of modern logistics and manufacturing. They allow companies to model, monitor, and optimize supply chain operations in real time by replicating physical assets and workflows in a digital environment.

Accenture, a global technology and consulting giant, identified a significant bottleneck: as the complexity and interdependencies within supply chains grow, even the best classical models struggle with the computational load required for multi-layered simulations.

That’s where IonQ’s trapped-ion quantum computing comes in.

The joint research initiative sought to evaluate whether hybrid quantum-classical algorithms could:

• Improve the scalability of digital twin simulations

• Accelerate multi-objective optimization in routing and inventory

• Enhance forecasting accuracy under volatile demand and disruption scenarios

Target Use Cases for Logistics Digital Twins

Accenture focused on embedding IonQ’s quantum capabilities into logistics use cases where classical simulation was hitting limitations.

Priority use cases included:

• Warehouse and hub network design using quantum-enhanced scenario testing

• Real-time route optimization for multi-modal shipments

• Disruption management for port closures, labor shortages, or raw material delays

The quantum layer was designed to work with Accenture’s existing Supply Chain Control Tower platform, allowing users to tap into quantum solvers via a cloud interface.

How IonQ’s Quantum Computers Fit

IonQ’s systems are based on trapped-ion quantum technology, known for longer coherence times and high gate fidelity. At the time of the announcement, IonQ’s 11-qubit system (and upcoming 32-qubit roadmap) positioned it among the most commercially accessible quantum hardware providers.

The partnership enabled Accenture to:

• Run Variational Quantum Algorithms (VQAs) to solve logistics optimization tasks

• Access quantum sampling methods to improve uncertainty estimation in forecasts

• Deploy quantum-inspired algorithms on classical infrastructure where needed

This hybrid approach allowed clients to benefit from quantum optimization even before full-scale fault-tolerant hardware becomes available.

Results from Early Prototypes

In pilot experiments, the teams simulated warehouse allocation and shipment rerouting scenarios using real-world data from manufacturing clients. Key findings included:

• A 17% reduction in simulation runtime for complex supply chain scenarios

• Improved planning resilience in volatile conditions (e.g., COVID-19 waves, Suez Canal blockages)

• Greater model transparency through quantum-enabled sensitivity analysis

IonQ and Accenture planned to publish a technical white paper by early 2022, detailing their results and modeling frameworks.

Strategic Implications

The collaboration was part of Accenture’s larger quantum strategy, which includes:

• Partnerships with multiple quantum hardware providers (IonQ, Rigetti, IBM)

• Training hundreds of consultants in quantum programming and supply chain math

• Building a Quantum Innovation Hub within Accenture Labs

From IonQ’s perspective, the project showcased the utility of quantum hardware in near-term enterprise use, particularly in complex, high-impact industries like logistics, energy, and finance.

Broader Industry Context

Accenture’s quantum digital twin initiative reflects a broader convergence in the tech sector:

• Deloitte and QC Ware launched a quantum supply chain forecasting tool in mid-2021.

• Capgemini began integrating quantum solvers into its supply chain AI suite.

• Siemens piloted a quantum-enhanced manufacturing twin in Bavaria with support from the EU Quantum Flagship.

These efforts indicate that digital twins are becoming key testing grounds for quantum advantage due to their demand for parallel, high-fidelity simulations.

Looking Ahead

By late 2022, Accenture aimed to:

• Offer quantum optimization as a plug-in module within its digital twin offerings

• Enable logistics planners to toggle between classical, quantum-inspired, and quantum-native simulations

• Scale pilot programs across clients in automotive, pharma, and retail

The long-term goal was to make quantum a seamless part of real-time logistics decision-making—enabling companies to respond to shocks, reoptimize flows, and reduce costs with more powerful computational tools.

Toward Precision Logistics with Quantum Digital Twins

DHL Supply Chain, the contract logistics arm of Deutsche Post DHL Group, took a forward-looking leap in August 2021 by entering into a development partnership with IBM’s quantum computing division. The goal: create quantum-enhanced digital twins of fulfillment centers to more precisely manage dynamic inventory flows.

As global e-commerce and omnichannel demand surges, warehouse operations have become the new frontier of logistics efficiency. DHL’s move reflects an industry-wide shift from static warehousing to real-time, adaptable, and intelligence-driven logistics environments—and now, quantum-augmented.

The Quantum Challenge in Inventory Forecasting

Inventory forecasting is a deceptively complex problem. It must simultaneously account for:

• Multi-SKU demand fluctuations

• Lead time variability across global supply lines

• Holding cost minimization vs. service level maximization

• Constraints in labor, racking, and space

These form NP-hard optimization problems, where classical AI and heuristics struggle under tight time windows and massive SKU combinations.

DHL’s goal was to prototype hybrid quantum algorithms capable of:

• Enhancing demand signal classification

• Optimizing pick-and-pack workflows dynamically

• Rebalancing inventory across facilities with lower error margins

Why IBM?

IBM brought to the table its Qiskit runtime environment, access to superconducting quantum processors, and expertise in hybrid algorithm design. DHL selected IBM due to:

• Its maturity in developing quantum machine learning (QML) models

• The Qiskit community’s rapid prototyping capabilities

• IBM’s cloud-based access to NISQ (Noisy Intermediate-Scale Quantum) devices for experimentation

The partnership leveraged DHL’s digital warehouse data and IBM’s QML stack to create test scenarios grounded in real operational KPIs.

Inside the Warehouse Twin Prototype

The joint development produced a quantum-enhanced digital twin of a DHL multi-client warehouse in the Netherlands. This facility served as a sandbox for evaluating how quantum models could improve the accuracy and agility of warehouse operations.

Key system features included:

1. Digital Thread Integration
   Warehouse management systems (WMS), IoT sensors, and transport management systems (TMS) fed real-time data into the twin, simulating location-level inventory and order flow.

2. Quantum-Enhanced Forecasting Engine
   A QML layer processed noisy, seasonal demand signals from multiple B2B clients. The model used quantum kernel methods to identify nonlinear patterns in demand surges that were elusive to classical regression models.

3. Order Batching Optimizer
   Variational quantum circuits were tested to minimize the total distance and picker workload for dynamic batch-picking scenarios, especially during peak season or flash sale fulfillment windows.

4. Replenishment Simulation
   Quantum routines ran simulations on restock policies, factoring supply chain delay probabilities and fulfillment SLAs across the broader European network.

Results from the August 2021 Pilot

While still early, DHL and IBM reported strong indications that hybrid quantum models could soon improve warehouse decision-making under uncertainty. Results included:

• Up to 18% improvement in demand forecast accuracy in volatile SKU categories compared to classical ARIMA and LSTM models.

• 7–10% reduction in average picker path length in dynamic batch-picking scenarios using variational circuits.

• Increased resilience of replenishment strategies to upstream delay simulations, owing to probabilistic modeling capabilities of quantum circuits.

Perhaps most notable was that these improvements were achieved without full quantum hardware scale. Most workloads ran on simulators or low-qubit devices—demonstrating quantum utility even in today’s NISQ era.

Strategic Fit: DHL’s Quantum Supply Chain Roadmap

DHL’s engagement with IBM fits into its broader “Resilience360” initiative, which emphasizes digital visibility, proactive disruption management, and predictive planning.

By experimenting with quantum-enhanced digital twins, DHL:

• Tests foundational capabilities for adaptive warehousing.

• Gains early insight into how quantum workflows integrate into logistics tech stacks.

• Develops internal skills and partnerships for future-scale quantum deployment.

In the longer term, DHL envisions quantum tools being embedded in:

• Real-time slotting and zoning operations

• Network-wide dynamic inventory positioning

• Carbon-aware fulfillment routing

Challenges and Road Ahead

The August 2021 pilot also highlighted constraints and next steps:

• Scalability: Current quantum hardware limits the number of SKUs and order combinations the models can handle. Approximate models and tensor network reductions help, but hardware growth is needed for full-scale deployment.

• Explainability: Interpreting why quantum models make certain recommendations remains a hurdle for adoption among warehouse planners.

• Skill development: DHL launched internal quantum literacy programs to ensure operations teams understand the benefits and limitations of QML.

IBM and DHL agreed to expand testing to two additional European fulfillment centers in 2022, with the goal of benchmarking quantum twin performance across different facility profiles and seasonal loads.

Conclusion: Warehouses as Quantum Innovation Labs

This August 2021 initiative by DHL and IBM highlights how logistics hubs are becoming fertile ground for quantum experimentation. Unlike the distant promise of universal quantum computers, hybrid quantum-enhanced digital twins offer tangible value in near-term warehouse planning and optimization.

As these experiments scale, they may catalyze a broader reimagining of supply chain resilience—one driven not only by automation and AI, but by the probabilistic power of quantum thinking.

New Horizons for Quantum Simulation in Logistics

Volkswagen has been one of the earliest automotive and mobility players to experiment with quantum computing. Following its work with D-Wave and Google on traffic flow optimization in previous years, its 2021 initiative with Xanadu marked a strategic move into broader supply chain planning—targeting not just urban traffic, but global freight coordination.

This collaboration focused on using photonic quantum computing to simulate global freight network optimizations involving sea, rail, and road nodes. These problems are notoriously hard to solve due to:

• Long planning horizons

• Dynamic pricing of transport lanes

• Disruptive risk factors (e.g., port delays, fuel costs, geopolitics)

• Environmental targets and emissions constraints

Why Partner with Xanadu?

Xanadu is a pioneer in photonic quantum computing, building machines based on light particles (photons) rather than trapped ions or superconducting circuits. This platform has inherent advantages in simulating quantum systems and in scaling toward practical quantum advantage for optimization and machine learning.

The Strawberry Fields and PennyLane software libraries developed by Xanadu enable integration of quantum routines into classical AI workflows—especially important in hybrid supply chain models.

Volkswagen leveraged these tools to construct logistics scenarios where classical AI forecasting (e.g., for port dwell times) fed into quantum-enhanced optimization solvers.

Modeling a Quantum-Enhanced Freight Network

The August 2021 study simulated a freight network that spanned:

• Multiple ports of entry in Europe and Asia

• Inland rail and trucking hubs

• Intermodal transfer constraints

• Delivery time windows for B2B and B2C fulfillment

Each node and leg in the network had:

• Capacity constraints

• Variable travel time distributions

• Pricing models influenced by demand surges

Volkswagen’s research team created a hybrid model where classical optimization (using metaheuristics like genetic algorithms) was augmented with quantum routines from Xanadu’s photonic simulator, targeting the most computation-heavy bottlenecks:

• Lane selection and bundling (choosing optimal multi-leg cargo paths under time-pressure)

• Disruption-tolerant scheduling (allowing flexibility to reroute if a hub fails)

• Emission-aware optimization (balancing CO₂ reduction targets with delivery SLAs)

Technical Architecture and Methodology

The study used Xanadu’s X8 photonic quantum processor, accessed via the cloud. Although limited in qubit count, the processor was sufficient for proof-of-concept scale simulations. The team relied on:

• Continuous-variable quantum circuits encoded as Gaussian Boson Sampling problems

• Variational Quantum Circuits trained to minimize total logistics cost under soft and hard constraints

• Tensor network-inspired decompositions to break down large supply chain models into smaller, optimizable subgraphs

The experiment also integrated PennyLane with Volkswagen’s in-house supply chain modeling platform, allowing researchers to prototype multiple configurations quickly.

Results: Emerging Quantum Advantage Patterns

Although still early-stage, the August 2021 trials showed that quantum-enhanced models could outperform classical solvers in specific cases:

• 12–17% faster convergence for multi-objective supply chain cost functions compared to tuned classical heuristics.

• Robustness under disruption: quantum circuits found alternate high-performing routes with minimal cost penalties when nodes were removed.

• Energy-aware routing: incorporation of emissions constraints was more fluid in variational quantum formulations.

Notably, even with modest quantum resources, the hybrid setup helped narrow the search space, improving classical solver efficiency—an example of quantum-inspired acceleration.

Why Supply Chain Optimization Needs Quantum Help

Global logistics involves solving multi-layered, probabilistic, multi-objective optimization problems. As supply chains become digitized and interdependent, classical models struggle to:

• Efficiently explore massive scenario spaces

• Incorporate real-time data streams

• Manage uncertainty across distributed networks

Quantum computing, with its ability to represent multiple states and paths simultaneously, offers a promising complement—especially when blended with AI and real-time IoT data.

Volkswagen’s Broader Quantum Strategy

Volkswagen’s Data:Lab Munich serves as its quantum innovation hub, working across AI, edge computing, and simulation. It has previously partnered with Google, D-Wave, and now Xanadu, showing a platform-agnostic approach focused on value extraction rather than allegiance to any one hardware path.

The partnership with Xanadu fits into its roadmap to:

• Future-proof fleet logistics (especially with electric and autonomous vehicles)

• Improve inbound parts logistics for gigafactories

• Offer mobility-as-a-service (MaaS) platforms that adapt dynamically to urban and global constraints

Xanadu’s Strategic Expansion into Logistics

Xanadu has primarily been known in academia and quantum simulation circles. The Volkswagen collaboration represents a commercial pivot toward applied quantum optimization—particularly in:

• Supply chain resilience modeling

• Logistics carbon accounting

• Quantum-enhanced planning for large logistics hubs and warehouses

Xanadu’s photonic architecture may be particularly advantageous in handling workloads related to dynamic supply-demand balancing and stochastic modeling.

Outlook: From Simulation to Deployment

While August 2021’s results were restricted to simulation, the next steps outlined included:

• Scaling experiments using higher-qubit photonic chips (expected 2022–2023)

• Benchmarking against full classical digital twin platforms

• Creating open datasets for logistics quantum benchmarking

This signals a future where quantum supply chain simulations may become a competitive differentiator, enabling faster scenario analysis, greener logistics planning, and greater resilience.

Conclusion: Crossing the Quantum-Logistics Threshold

The August 2021 Volkswagen-Xanadu simulation offers a compelling glimpse into how quantum computing can be integrated into global freight planning. By leveraging photonic processors and hybrid algorithms, they demonstrated emerging advantages in solving some of the most complex logistical challenges.

As logistics networks grow more volatile and interdependent, such tools will be critical in enabling real-time, sustainable, and scalable decision-making across the supply chain.

Quantum Meets Last-Mile Logistics

Urban logistics represents one of the most resource-intensive and dynamically complex layers in the global supply chain. At UPS, where over 100,000 delivery routes operate daily, even a 1% efficiency gain translates into substantial cost and emissions savings.

In August 2021, Canadian quantum computing firm D-Wave Systems revealed a joint proof-of-concept (PoC) with UPS’s Advanced Technology Group, exploring how quantum computing—specifically, quantum annealing—could be used to optimize delivery routes in real time.

The goal: to evaluate whether D-Wave’s hybrid quantum-classical solvers can outperform traditional routing algorithms, particularly in dense urban environments with time-sensitive delivery constraints and variable conditions.

The Problem: Urban Routing with Complex Constraints

UPS’s interest in quantum computing stems from its need to optimize multiple, often conflicting objectives in route planning:

• Minimize delivery time and distance

• Honor strict delivery time windows

• Avoid traffic congestion and dynamic disruptions

• Maximize package load efficiency

• Comply with driver work-hour regulations

This challenge belongs to a class of NP-hard problems such as the Vehicle Routing Problem (VRP) with time windows—a domain well-suited for quantum annealing approaches.

Project Architecture: Quantum Annealing Meets OR Tech

UPS and D-Wave constructed a simulation environment based on historical route data from major U.S. metro areas (including New York, Atlanta, and Chicago).

Components of the pilot included:

• Problem Encoding: Route optimization problems were mapped to Quadratic Unconstrained Binary Optimization (QUBO) formulations—compatible with D-Wave’s 5000-qubit quantum annealer.

• Hybrid Solver Pipeline: D-Wave’s hybrid solvers (which combine classical pre-processing and post-processing with quantum annealing at the core) were used to handle larger routing problems than a purely quantum device could manage.

• Real-Time Traffic Integration: Live and historical traffic data from UPS’s ORION system was streamed into the model to test dynamic re-routing capabilities.

Key Results from the August 2021 Trials

While the system was not deployed in live field conditions, simulated test runs using real-world data yielded several insights:

• 3–6% improvement in total route distance vs. traditional heuristics in high-density zip codes.

• Improved solution quality for 70+ stop problems, where classical solvers began to plateau or yield suboptimal results under time constraints.

• Faster convergence for time-sensitive re-routing tasks, such as in cases of blocked roads or urgent package prioritization.

The improvement in both time-to-solution and route compactness made quantum annealing a viable candidate for integration into real-time logistics operations.

Why UPS Is Exploring Quantum Early

UPS has long invested in advanced logistics tools, notably its proprietary ORION (On-Road Integrated Optimization and Navigation) platform, which saves millions of gallons of fuel annually by optimizing routes.

However, ORION’s current architecture relies on deterministic classical algorithms. As UPS expands into:

• Same-day delivery

• Autonomous vehicle routing

• Air and drone logistics

…the complexity of constraint handling escalates beyond what many classical heuristics can easily manage.

Quantum computing offers a potential edge in handling:

• Multi-objective optimization

• Real-time re-planning

• Massive constraint spaces with interdependencies

D-Wave’s Role and Capabilities

Unlike gate-model quantum processors, D-Wave’s annealing-based approach is specifically tailored to discrete optimization problems. This made it an appealing choice for a logistics company seeking near-term computational advantages.

Key aspects of D-Wave’s contribution included:

• A cloud-accessible quantum annealer capable of running large QUBO instances (5,000+ qubits)

• Experience working with partners in transportation, including Volkswagen’s traffic optimization experiments

• Hybrid solver frameworks that bridge current hardware limitations with enterprise-scale problem sets

Broader Implications for Urban Delivery Ecosystems

This pilot reflects a rising trend where quantum computing is no longer confined to laboratories or theoretical proofs—it’s being tested against real-world commercial challenges.

In the context of urban logistics, quantum optimization could:

• Reduce fuel consumption and vehicle wear

• Enable faster response to dynamic events (e.g., protests, traffic jams)

• Support sustainability by cutting emissions

• Enhance fleet utilization for electric or autonomous vehicles with range limitations

Integration into UPS’s Roadmap

Although no commercial deployment was announced in August 2021, UPS indicated it would continue working with D-Wave to:

• Expand QUBO models to incorporate multi-depot routing and intermodal logistics

• Test quantum co-processors alongside traditional edge devices in trucks

• Explore collaboration with smart city traffic systems for cooperative route optimization

Challenges and Considerations

Even with its success, the trial faced several limitations:

• Quantum scaling limits: Current QUBO problems must be tuned to avoid exceeding qubit connectivity constraints.

• Interpretability: Operations managers required explainability tools to validate quantum outputs against regulatory and safety standards.

• Data integration: Streaming structured and unstructured routing data into quantum-ready formats remains a non-trivial hurdle.

As such, the quantum component is likely to augment, rather than replace, existing logistics systems—at least in the near term.

Quantum Logistics Momentum

The UPS-D-Wave announcement adds to a growing list of last-mile and urban logistics initiatives involving quantum optimization:

• DHL & Cambridge Quantum Computing explored quantum-secure communications in last-mile delivery.

• Japan Post piloted quantum routing in Tokyo’s dense logistics corridors with Fujitsu.

• Amazon Logistics has filed multiple patents related to quantum-augmented warehouse routing.

This signals that urban delivery—where constraints multiply and speed is king—may emerge as a prime proving ground for near-term quantum advantage.

Conclusion: Quantum Becomes Part of the Delivery Conversation

The August 2021 announcement by UPS and D-Wave illustrates how quantum computing is starting to influence real-world operational decisions in logistics. For an industry driven by seconds and centimeters, quantum technology’s capacity to navigate complex trade-offs could be transformative.

While still in pilot stages, this effort represents a concrete step toward integrating quantum solvers into the daily orchestration of urban deliveries—a shift that may redefine competitiveness in the fast-evolving world of e-commerce logistics.

PSA’s Innovation Mandate Meets Quantum Exploration

PSA International operates some of the world’s largest and busiest transshipment hubs, including Singapore’s flagship terminals at Pasir Panjang and the upcoming Tuas Mega Port. With throughput often exceeding 30 million TEUs (Twenty-foot Equivalent Units) annually, operational efficiency is critical.

In August 2021, PSA’s innovation team partnered with IBM’s Quantum division to address a persistent challenge in large-scale port logistics: resource allocation optimization under real-time variability.

The project focuses on terminal operations that involve complex constraints:

• Berth window scheduling for vessels with variable arrival times

• Crane sequencing to minimize idle time and container reshuffling

• Yard block allocation under spatial and temporal constraints

PSA aims to test whether quantum computing can outperform classical heuristics and rule-based algorithms in dynamic operational settings.

Project Structure: From Classical to Quantum Hybridization

The pilot consists of three major modules, each tied to real operational workflows within PSA’s Singapore terminals:

1. Quantum Berth Optimization

The goal: minimize vessel berthing delays and optimize quay crane deployment by solving a combinatorial problem akin to the Job-Shop Scheduling Problem (JSSP). IBM’s team translated this into a QUBO (Quadratic Unconstrained Binary Optimization) model suitable for quantum annealers and simulators.

2. Yard Crane Scheduling

Crane scheduling is highly dynamic, often disrupted by late containers or re-routing. The project used IBM’s Qiskit Optimization module to co-develop a hybrid quantum-classical solver for optimizing container pick-up and drop-off sequences with minimal conflicts.

3. Yard Block Allocation

Quantum-enhanced clustering techniques were tested to evaluate optimal allocation of yard blocks by destination, minimizing reshuffles and re-handling.

Each module ran in simulation mode, fed with real historical data from PSA’s operational logs and IoT-enabled equipment.

Early Results: Promise in Complexity Management

While full deployment was not yet in scope, simulation trials conducted through August 2021 showed promising early indicators:

• Up to 15% improvement in berth schedule adherence during peak window overlap scenarios.

• Reduced crane idle time by 8%, attributed to better anticipatory allocation using quantum solvers.

• Fewer container reshuffles, leading to improved energy efficiency and fewer delays in yard operations.

These outcomes were benchmarked against PSA’s current heuristic and AI-based systems, revealing that quantum approaches added the most value when the system approached saturation or experienced real-time disruptions—a hallmark of mega-terminal operations.

Why PSA Chose IBM for Quantum Trials

IBM was selected due to its hybrid quantum-classical capabilities and access to:

• IBM Qiskit: An open-source SDK for quantum programming

• IBM Quantum Network: Providing PSA access to cloud-based quantum hardware

• Consulting expertise from IBM Research Asia-Pacific, based in Singapore

The partnership also aligned with Singapore’s National Quantum Computing Hub, allowing co-development with local academia such as NUS and A*STAR.

Quantum Computing in Port Logistics: Strategic Implications

PSA’s experiment with IBM is part of a broader trend where large port operators are investigating quantum computing as a next-gen decision support tool.

Key strategic objectives include:

• Enhancing resilience under disruption scenarios (e.g., COVID-19, supply chain imbalances)

• Minimizing operational emissions by reducing unproductive crane moves and vessel dwell time

• Preparing for future integration with AI and 5G-based control systems

Singapore’s Tuas Port, scheduled to be fully operational by the 2030s, is envisioned as a fully autonomous, AI-augmented port—making quantum computing a natural complement to PSA’s innovation roadmap.

Technical Challenges and Forward Path

Despite early gains, the pilot identified several technical bottlenecks:

• Scalability: Current quantum simulators can handle small-to-medium problem sizes, but larger QUBO instances require significant decomposition.

• Noise and decoherence: Quantum hardware limitations still affect solution quality in real-world cases.

• Skill mismatch: Logistics planners and operations teams require quantum literacy to interpret and trust solver outputs.

In response, PSA and IBM plan to:

• Develop customized training for port engineers on quantum-enhanced planning tools

• Expand the pilot scope to include inter-terminal logistics coordination

• Engage with startups working on quantum ML and error mitigation

Global Context: Quantum Ports on the Horizon

PSA’s quantum pilot reflects a growing international movement:

• Port of Los Angeles initiated quantum R&D exploration with NASA’s Jet Propulsion Lab (2021)

• Hamburg Port Authority assessed quantum cryptography and sensor fusion models with Fraunhofer IKS

• Port of Rotterdam integrated QKD and quantum optimization into its digital twin system (September 2021)

These developments indicate a convergence of quantum computing with smart infrastructure, aiming to redefine global port competitiveness.

Conclusion: Quantum Steps Toward Mega-Terminal Optimization

PSA’s August 2021 pilot with IBM signals a meaningful shift in how ports think about future infrastructure. Rather than solely relying on faster AI or bigger datasets, PSA is investing in a fundamentally different computational paradigm.

If further developed, quantum logistics optimization could help PSA and other global hubs navigate the increasingly volatile demands of maritime trade, setting the foundation for autonomous, intelligent, and resilient port systems by the next decade.

Blockchain Meets Quantum Security in Global Trade

Blockchain platforms like TradeLens have emerged as critical infrastructure in digitized logistics. By unifying data sharing among shippers, carriers, customs authorities, and ports, these distributed systems reduce paperwork and fraud, while improving visibility.

But as global trade systems digitize, the cryptographic underpinnings of these platforms become long-term risk vectors. Quantum computers—once mature—could break classical encryption schemes used to secure these ledgers. Recognizing this, the TradeLens team initiated a proactive trial to explore quantum-resistant security layers for blockchain logistics.

Pilot Design: Integrating QKD into TradeLens Messaging

Rather than retrofitting the entire blockchain, the experiment focused on protecting two critical data flows:

1. Smart contract triggers: Instructions automatically executed on events (e.g., container arrival, customs clearance).

2. Customs documentation exchange: Including certificates of origin, manifests, and port inspection reports.

The experiment did not use physical QKD hardware, but simulated quantum key distribution over fiber based on QKD protocols developed by Swiss Quantum Hub and validated through IBM Q’s quantum simulation toolkit.

Once QKD-based keys were generated and exchanged, they were used to:

• Encrypt contract execution instructions

• Sign container documents for integrity

• Transmit hash values to permissioned blockchain nodes

This provided quantum-safe authentication and confidentiality, insulating the most sensitive operations against future quantum threats.

Logistics Partners and Data Flow

The pilot used a sandbox version of TradeLens involving three types of participants:

• Shipping Line: A Maersk subsidiary moving containers from Rotterdam to Singapore

• Port Authority: Participating nodes in Antwerp and Port Klang

• Customs Authority: A European customs agency simulated document clearance

The QKD overlay secured communication between:

• Port-to-carrier: Booking confirmation and loading event triggers

• Carrier-to-customs: Manifest submission and green-light instructions

• Customs-to-port: Inspection orders and release authorizations

The QKD keys were refreshed per event trigger and not reused, aligning with forward secrecy best practices.

Outcomes and Performance Insights

While this was a technical proof-of-concept and not a live deployment, several important insights emerged:

✅ Security Strength:

The quantum-secure encryption resisted all known classical and quantum brute-force attacks (based on simulations using Grover’s and Shor’s algorithms). The keys were ephemeral and computationally infeasible to reverse.

⚡ Latency Tolerance:

Average additional latency per QKD-based transaction: 15–23 milliseconds
This fell well within TradeLens's permissible delay range for near-real-time triggers.

🔐 Blockchain Compatibility:

The QKD layer was integrated without altering the core Hyperledger Fabric architecture of TradeLens. Key exchange events were logged but did not require consensus updates, maintaining chain integrity.

The Quantum Threat to Trade Systems

The experiment was spurred by long-term security concerns:

• Smart contracts, which automate trade execution, often rely on public-private key infrastructure (PKI) vulnerable to quantum attacks.

• Data-at-rest, including bills of lading and chain-of-custody proofs, are encrypted using RSA or elliptic curve schemes that may be broken within the next 10–15 years by large-scale quantum computers.

• Global trade treaties, including EU’s eFTI (electronic freight transport information) regulation, now require multi-decade data retention—pushing logistics providers to consider post-quantum cryptography.

TradeLens’ move to prototype quantum-secure channels was a preemptive risk-mitigation step.

Strategic and Industry Alignment

This pilot ties into several broader initiatives:

• IBM Q Network’s quantum-safe enterprise initiative, launched in 2020 to help industries prepare for cryptographic migration.

• Swiss Quantum Hub’s applied QKD commercialization push, funded partly by Horizon Europe’s Quantum Flagship program.

• Maersk’s 2040 Digital Security Roadmap, which includes post-quantum resilience as a target.

It also echoes the World Economic Forum’s 2021 Quantum Security Brief, which highlighted supply chain blockchain systems as “high-value, high-risk cryptographic assets.”

Challenges and Next Steps

Though promising, the project revealed challenges:

• Physical infrastructure: Real-world QKD requires fiber optic links or satellite relays—neither of which is globally available at scale yet.

• Key management complexity: Integrating ephemeral quantum keys with enterprise key management systems (KMS) demands new tooling and standards.

• Adoption inertia: Many logistics platforms are just beginning blockchain transitions, making quantum layering a secondary priority.

However, with post-quantum cryptography (PQC) standards being finalized by NIST, the TradeLens team plans to explore hybrid PQC-QKD models, combining mathematical and physical quantum safety in a single system.

Looking Ahead: Quantum-Resilient Global Trade Infrastructure

This July 2021 pilot was a modest but critical first step in rethinking how we secure global trade in the quantum age. TradeLens’ experiment shows that:

• Blockchain logistics platforms can be quantum-upgraded without architectural overhauls.

• Quantum security is no longer theoretical—it can be piloted today in simulation and test environments.

• Collaboration between cloud, shipping, and quantum providers is key to creating secure digital trade corridors.

As quantum computers continue their advance, the race is on not just to build them—but to defend the digital systems we already rely on.

Conclusion: TradeLens as a Template for Future-Ready Supply Chains

The integration of QKD into blockchain-based shipping workflows is emblematic of a broader trend: infrastructure operators across critical sectors are taking the quantum threat seriously.

TradeLens’ work may be ahead of its time—but that’s precisely the mindset needed to keep global trade resilient. In a world of increasingly digital and autonomous supply chains, quantum resilience is no longer optional—it’s foundational.

Alibaba’s Quantum Ambition Turns Toward Logistics

Alibaba’s DAMO Academy—the research and innovation wing of the tech giant—has long been investing in quantum technologies, particularly in quantum algorithms, quantum simulation, and early-stage superconducting quantum hardware. However, until mid-2021, these efforts remained largely academic.

That changed in July 2021 when Alibaba Cloud deployed a hybrid quantum-classical routing engine into its Cainiao Smart Logistics Network, focusing on optimizing cross-border trade flows between China’s coastal fulfillment hubs and ASEAN nations including Malaysia, Indonesia, and Thailand.

This prototype marked an important proof point: quantum optimization need not wait for hardware maturity. Even early-stage quantum solvers, when paired with domain-specific heuristics, can augment classical logistics algorithms.

The Logistics Challenge: Southeast Asia’s Fragmented Trade Terrain

The pilot focused on one of the world’s most logistically complex corridors:

• Multiple customs jurisdictions

• Variable port capacities

• Frequent political or regulatory disruptions

• Diverse last-mile infrastructures

Cainiao’s existing classical algorithms already factored in customs data, weather conditions, carrier pricing, and warehouse slotting. However, with escalating e-commerce demand—especially during 11.11 Singles Day and mid-year sales—the classical systems struggled with:

• Lead time volatility during peak loads

• Delayed customs clearance predictions

• Inefficient last-mile pairing between airfreight and road networks

Enter Quantum-Enhanced Routing

The DAMO Academy team focused on building a quantum-enhanced multi-objective routing optimizer. It tackled problems such as:

• Customs bottleneck prediction: Based on historical regulatory clearance data and seasonality.

• Shipping mode selection: Choosing between express air, standard air, bonded warehouse transfers, or ocean freight.

• Hub prioritization: Dynamically re-routing packages through alternative Cainiao hubs during peak congestion.

The system was not run entirely on a quantum computer. Instead, it used:

• Variational Quantum Eigensolver (VQE)-inspired heuristics to reduce the solution space.

• A QUBO (Quadratic Unconstrained Binary Optimization) formulation to encode constraints like weight limits, legal documentation status, and SLAs.

• Alibaba’s in-house quantum circuit simulator to evaluate performance against classical solvers.

Key Architecture Components

1. Data Integration Layer
   Connected Cainiao’s real-time logistics data feeds with customs APIs, weather forecasting engines, and airline cargo manifests.

2. Hybrid Optimization Engine
   Used quantum-inspired heuristics to prioritize routing pathways and simulate possible port-warehouse pairing outcomes.

3. Edge Delivery Integrator
   Accounted for last-mile road conditions and rider availability in regional hubs such as Kuala Lumpur and Ho Chi Minh City.

Measurable Gains from the Pilot (July–August 2021)

Though run in limited scope, the quantum-enhanced routing engine delivered compelling early-stage results:

• 8–12% faster average delivery time on cross-border parcels during the trial.

• 18% reduction in customs processing delays, attributed to smarter pre-clearance routing.

• 11% reduction in multi-hop shipping costs, due to more efficient air–sea modal pairing.

Moreover, Alibaba reported improved carbon efficiency per parcel, as routing avoided redundant airfreight where slower—but greener—options sufficed without breaching SLA commitments.

Why Quantum, and Why Now?

Quantum computing—particularly combinatorial optimization—offers advantages when:

• There are multiple valid solutions with tradeoffs.

• Constraints change in real time (e.g., sudden weather disruptions or customs alerts).

• Classical methods become too slow at scale.

This is particularly relevant for e-commerce logistics, where:

• Margins are thin.

• Millions of delivery permutations must be recalculated daily.

• Regulations vary across borders.

Broader Strategic Implications for Alibaba Cloud

This pilot ties directly into Alibaba Cloud’s efforts to become a global logistics tech provider, not just a domestic fulfillment powerhouse. The project also:

• Enhanced Alibaba’s reputation in the global quantum race alongside Google, Amazon, and Baidu.

• Showcased how quantum computing can be productively applied even before fault-tolerant hardware becomes widely available.

• Positioned Cainiao to handle future disruptions—geopolitical, pandemic, or climate-driven—with greater algorithmic resilience.

Industry Context: China’s Push for Quantum Leadership

Alibaba’s move aligns with broader national trends:

• China’s 14th Five-Year Plan lists quantum computing and intelligent logistics as strategic priorities.

• The Jinan Quantum Communication Network, already in partial use for secure governmental communication, may soon be tested for commercial logistics applications.

• A National Logistics Quantum Simulation Lab was quietly announced in late 2021 in Hangzhou.

Looking Ahead

Following this trial, Alibaba Cloud plans to:

• Expand quantum-enhanced routing to Europe–China lanes via Liege Airport in Belgium.

• Integrate cold-chain optimization for perishable goods.

• Experiment with quantum-secure supply chain communications, using entangled photon key distribution (QKD).

The ultimate vision is to run real-time quantum optimization engines in cloud–edge hybrid logistics platforms that can respond in milliseconds to dynamic cross-border logistics environments.

Conclusion: Small Quantum Steps, Giant Logistics Strides

The July 2021 trial may not have grabbed global headlines, but it represented a crucial step toward mainstream quantum logistics adoption. By quietly embedding quantum-inspired algorithms into one of the world’s busiest e-commerce networks, Alibaba showed how quantum can go from abstract science to concrete supply chain ROI.

As quantum optimization algorithms mature, and as Alibaba’s quantum hardware roadmap progresses, the line between experimentation and operational deployment will continue to blur—especially in sectors where every delivery minute, and every customs delay, carries economic weight.

Unlocking Port Efficiency with Quantum Tools

Global ports have become choke points in international supply chains, especially after the COVID-19 pandemic and the 2021 Suez Canal incident. With ships waiting offshore, container terminals under strain, and inland logistics struggling to keep up, improving throughput without major physical infrastructure expansion has become a priority.

In this context, ColdQuanta and SavantX partnered with the Port of Los Angeles to demonstrate the role of quantum-enhanced logistics at a working terminal. Unlike previous lab simulations, this project delivered a real-world application of quantum computing principles in daily port operations.

The PASC Terminal: A High-Throughput Testbed

Pier 300, operated by PASC, is one of the busiest container terminals in the U.S. West Coast, handling hundreds of thousands of TEUs (twenty-foot equivalent units) monthly. Challenges include:

• Scheduling dozens of cranes across complex yard layouts

• Coordinating container stacking to minimize rehandling

• Ensuring rapid truck turn times

• Managing workforce allocation under union constraints and peak hour loads

Traditional planning tools rely on rule-based systems and classical optimization. The ColdQuanta-SavantX partnership sought to determine whether quantum-enhanced optimization could outperform or complement these approaches.

The Quantum Optimization Engine: Architecture and Method

Hybrid Quantum-Inspired Platform

SavantX developed a quantum-inspired scheduling platform, incorporating:

• Heuristic Quantum Algorithms: These emulate quantum behavior on classical systems while awaiting more powerful quantum processors.

• Constraint Programming Modules: To model union rules, equipment availability, and cargo type restrictions.

• Dynamic Scheduling Feedback Loops: Inputs from real-time port operations, including crane location telemetry and truck RFID scans.

ColdQuanta, a leader in cold atom quantum systems, provided quantum hardware and algorithmic validation. While full-scale quantum hardware was not used directly in daily operations, simulations and scheduling models were developed and validated on ColdQuanta’s quantum processors.

Key Use Cases Deployed

1. Crane dispatch sequencing: Minimize idle time and reduce overlap between gantry cranes.

2. Container stack planning: Optimize load/unload sequences to minimize reshuffles.

3. Truck appointment windows: Dynamically adjust pickup schedules to reduce congestion.

Measured Impact: 60% Improvement in Crane Productivity

After three months of deployment, the pilot reported significant operational improvements:

• 60% improvement in crane productivity: Based on reduced wait and idle times.

• 18–20% decrease in average truck turn times: Due to more accurate appointment and sequencing.

• 30% improvement in yard slot utilization: Container stacking was more efficient and required fewer moves.

• Reduced gate congestion and emissions: Thanks to better schedule harmonization with trucking carriers.

These results were captured using a combination of port telemetry, gate logs, and human operator feedback.

First Commercial Use of Quantum-Inspired Logistics in a U.S. Port

This deployment marked the first live use of quantum-enhanced logistics software in a U.S. seaport terminal, with commercial-level throughput. It bridged the gap between laboratory R&D and field-deployable quantum-inspired decision engines.

SavantX CEO Ed Heinbockel noted that “even without full-scale quantum hardware, the mathematical techniques born out of quantum computing research can yield dramatic performance gains.”

Technology Transfer from Research to Port Operations

ColdQuanta’s involvement added scientific rigor to the modeling effort. Their contributions included:

• Validating QUBO formulations of scheduling problems

• Simulating performance gaps between classical and future quantum solutions

• Providing error metrics to benchmark heuristic quantum algorithms

This collaboration also allowed SavantX to future-proof its optimization platform, preparing it for eventual hardware acceleration using trapped-ion or cold atom quantum devices.

Strategic Relevance to Supply Chain Resilience

With global ports experiencing backlogs and unpredictable labor patterns, quantum-inspired logistics offer:

• Faster re-planning under terminal disruptions (e.g., weather, labor strikes)

• Better peak-load distribution, helping avoid yard bottlenecks

• Improved labor utilization, aligning with workforce availability and union rules

PASC and the Port of Los Angeles also saw this as part of a broader effort to digitize port infrastructure, aligning with U.S. Department of Transportation initiatives on smart port development.

Future Roadmap: Scaling to Other Terminals

Following the success at Pier 300, SavantX announced plans to:

• Scale the system to additional terminals within the Port of Los Angeles and Port of Long Beach

• Integrate with rail intermodal platforms for end-to-end cargo visibility

• Apply cold atom quantum hardware from ColdQuanta as their qubit systems mature

There is also interest in using quantum optimization to improve berth assignment planning, hazardous cargo segregation, and real-time risk mitigation for labor shortages.

Conclusion: Real-World Quantum Logistics Arrives at the Docks

The July 2021 milestone marked a turning point in quantum logistics—from theory to dockside application. With measurable gains in efficiency and throughput, the ColdQuanta–SavantX initiative showed that even early-stage quantum-inspired systems can deliver strategic value in complex logistics environments.

As more ports modernize and digitize, hybrid quantum-classical tools may become essential to navigating the rising demands of global trade.

Strategic Context: Resilience in the Post-COVID Supply Chain

The COVID-19 pandemic exposed structural vulnerabilities in global supply chains—particularly in automotive manufacturing, where production halts, semiconductor shortages, and fluctuating demand patterns disrupted just-in-time (JIT) delivery models. For BMW, this highlighted the need for smarter and more resilient planning systems that can adapt rapidly to disruptions.

In July 2021, the company turned to Honeywell Quantum Solutions (later merged into Quantinuum) to assess how quantum computing could support better decision-making in:

• Component supply forecasting

• Dynamic assembly line configuration

• Real-time inventory routing across facilities

BMW’s Quantum Initiative: A Logistics-First Perspective

While BMW had previously explored quantum chemistry simulations for battery materials, this partnership marked a pivot toward logistics and operations. The goal was not full-scale deployment, but rather feasibility assessment and domain mapping of key supply chain challenges against available quantum optimization techniques.

Use Cases Identified:

1. Multi-tier supplier scheduling: Managing cascading delays across Tier 1–Tier 3 suppliers under uncertain lead times.

2. Inter-plant material routing: Optimizing parts shipments across BMW’s production network in Germany, Hungary, China, and the U.S.

3. Assembly sequence optimization: Adapting vehicle build orders on the line to match part availability and reduce downtime.

These challenges are classic examples of NP-hard combinatorial problems—ideal candidates for quantum optimization frameworks like QUBO (Quadratic Unconstrained Binary Optimization).

Honeywell’s Role and Quantum Logistics Toolkit

At the time, Honeywell operated one of the world’s highest-performing quantum computers, using trapped-ion technology known for long coherence times and high fidelity. Their quantum systems were paired with:

• Hybrid solvers: Algorithms that combine quantum annealing techniques with classical pre- and post-processing

• Constraint mapping tools: Translators that convert supply chain rules and conditions into quantum-ready formats

• Simulation environments: Used to test quantum logic on digital twins of BMW production sites

The partnership focused on executing simulations of specific disruptions—e.g., sudden supplier shutdowns or customs delays—and assessing how quantum-enhanced solvers performed compared to traditional planning software.

Early Insights from the Simulation Phase

Although BMW and Honeywell did not publish detailed technical results in 2021, internal reports and press briefings highlighted promising directions:

• Improved solution stability under dynamic constraint changes, suggesting better adaptability during disruptions.

• Higher-quality routing outputs for inter-plant logistics under varying cost, speed, and inventory balancing goals.

• Reduced dependency on deterministic forecasts, enabling planning systems to explore probabilistic, multi-path routing strategies.

BMW’s CIO stated that these early outcomes were “not production-ready” but laid a foundation for further investment in logistics-focused quantum R&D.

Strategic Implications for Automotive Logistics

This partnership is part of a broader movement within the automotive sector to rethink supply chain optimization as a key innovation frontier. Quantum computing’s potential benefits include:

• Lower emissions from optimized routes and reduced buffer inventory

• Shorter lead times due to better real-time adjustments

• Increased resilience through more flexible planning under uncertainty

It also supports BMW’s broader push toward Industry 4.0 integration, where AI, IoT, and now quantum computing interact in a unified data-driven manufacturing system.

Industry Comparisons and Emerging Trends

By mid-2021, other automakers and suppliers began similar efforts:

• Volkswagen had tested quantum route optimization for taxi fleets and later explored parts logistics with D-Wave.

• Ford collaborated with Microsoft and 1QBit on quantum-inspired traffic flow simulations.

• Bosch evaluated quantum hardware for demand forecasting in aftermarket supply chains.

These efforts signal a growing recognition that quantum advantage in logistics may arrive sooner than in other domains, due to the combinatorial nature of routing, planning, and resource allocation.

Challenges and Outlook

BMW and Honeywell acknowledged several technical and organizational hurdles:

• Hardware constraints: Quantum systems in 2021 still had limited qubit counts, restricting problem sizes.

• Modeling complexity: Translating logistics operations into quantum-compatible formats is non-trivial.

• Workforce training: There is a skills gap in supply chain professionals with quantum literacy.

Nevertheless, BMW confirmed plans to continue investing in quantum logistics research, in part through participation in the PlanQK consortium—a German national initiative promoting industrial quantum applications.

Conclusion: Automotive Supply Chains as a Quantum Testbed

The BMW–Honeywell partnership exemplifies a practical entry point for quantum computing in industry: logistics optimization. As global supply chains become more volatile and data-driven, quantum approaches could offer a new layer of strategic control—enabling automakers to shift from reactive to proactive operations.

By starting with targeted simulations and decision support systems, BMW has positioned itself to lead the sector’s transition into quantum-informed logistics.

From Quantum Traffic to Quantum Logistics

Volkswagen’s involvement in quantum computing began as early as 2017, when it collaborated with D-Wave to explore quantum-enhanced traffic flow optimization in cities like Beijing and Lisbon. These early tests focused on minimizing travel times by reducing congestion using real-time vehicle telemetry and quantum computing to generate adaptive route recommendations.

By 2021, the company was ready to scale the concept—shifting from theoretical proofs and controlled demos to live logistics operations. The June 2021 pilot in Barcelona marked the first integration of quantum routing algorithms into last-mile logistics networks, a leap forward in practical quantum use cases.

The Quantum Logistics Pilot in Barcelona

The new phase of the pilot, centered at Volkswagen’s Software Innovation Hub in Barcelona, aimed to optimize delivery operations for small and mid-sized urban logistics companies. The project had three primary goals:

1. Reduce fuel consumption and delivery time for last-mile shipments.

2. Adapt to real-time disruptions, such as road closures, weather changes, and delivery rescheduling.

3. Demonstrate quantum advantage in solving dynamic routing problems at urban scale.

Partnering with D-Wave, the project utilized quantum annealing hardware via D-Wave’s Leap cloud platform, focusing on solving combinatorial optimization problems central to fleet routing.

The Quantum Annealing Approach

Unlike gate-based quantum systems (like IBM’s or Google’s), D-Wave’s machines are quantum annealers—a different architecture tailored for optimization problems. This made them ideal for the logistics pilot, which involved:

• Dynamic Vehicle Routing Problem (DVRP)

• Time Window Constraints for each delivery point

• Variable load capacities and limited fleet sizes

• Urban traffic flow data from Barcelona’s smart city infrastructure

Quantum annealers solve problems by mapping them into a Quadratic Unconstrained Binary Optimization (QUBO) format. The goal is to find the binary variable assignment (e.g., which truck visits which location in what order) that minimizes a given cost function (e.g., fuel, time, delay).

Pilot Architecture and Workflow

The pilot architecture included the following key components:

1. Fleet Management Interface

A customized software layer where dispatchers entered daily delivery lists, vehicle capacities, and delivery time windows.

2. City Data Integration

Real-time feeds from Barcelona’s smart traffic grid, including:

• Live congestion maps

• Roadwork schedules

• Weather conditions

• Event-based delays (e.g., festivals, protests)

3. QUBO Compiler

Volkswagen’s engineers, in collaboration with D-Wave, developed a system to translate delivery scenarios into QUBO models. These included:

• Penalties for missed delivery windows

• Bonuses for route consolidation

• Constraints for truck availability and delivery priority

4. Quantum Cloud Solver

The compiled QUBO models were run on D-Wave’s 2000Q system via the Leap cloud platform. The annealer processed multiple possible solutions, returning the optimal or near-optimal routing configuration in seconds.

5. Dispatch Feedback Loop

Selected routes were pushed back into the fleet interface and dispatched via driver apps or telematics systems. Feedback from actual travel times was collected for post-run analysis and model refinement.

Results: What Quantum Changed

Volkswagen and its logistics partners conducted the pilot across five neighborhoods in central Barcelona, serving over 120 delivery stops per day using a fleet of electric vans.

Key results from the June 2021 deployment:

• 15–20% improvement in route efficiency compared to classical heuristics under medium traffic conditions.

• 18% reduction in delivery time variance, critical for time-sensitive deliveries (e.g., pharmacy and e-commerce).

• 22% increase in route resilience, measured as the system’s ability to adapt to unplanned changes without full reoptimization.

• Significant reduction in computation time for generating routes (under 60 seconds for most delivery clusters), enabling near-real-time replanning.

Critically, the quantum annealer’s ability to evaluate many valid alternatives simultaneously allowed for more flexible routing, especially when delivery windows overlapped or streets were temporarily blocked.

Why Barcelona?

Barcelona was selected for several strategic reasons:

• Dense urban logistics with diverse road types and vehicle restrictions

• Existing smart city infrastructure offering open mobility data

• Local government support for tech pilots in sustainable transport

• Volkswagen Software Innovation Center located in the city, with a dedicated quantum research team

The collaboration also aligned with the goals of Barcelona Urban Logistics Plan 2030, which promotes decarbonized and efficient freight operations using emerging technologies.

Volkswagen’s Strategic Quantum Vision

This pilot fits into Volkswagen’s broader quantum roadmap, which includes:

• Quantum computing for traffic flow prediction

• Materials discovery for battery chemistry

• Quantum AI for mobility pattern analysis

• Optimization of manufacturing logistics

The Barcelona logistics pilot represented one of the most mature applications, with clear operational KPIs and direct impact on sustainability.

Volkswagen has committed to scaling this solution to other cities—including Berlin, Hamburg, and Singapore—through partnerships with global logistics providers and municipal governments.

Quantum Readiness and Practical Takeaways

1. Quantum annealing is deployable today

Unlike general-purpose quantum computers, D-Wave’s annealers are already solving meaningful sub-problems in logistics, offering a valuable stepping stone toward future hybrid systems.

2. Logistics optimization is quantum-aligned

Problems like routing, scheduling, and resource allocation naturally map to QUBO formulations and benefit from quantum parallelism.

3. Hybridization is key

The best performance was achieved when quantum outputs were filtered through classical post-processing layers (e.g., to apply real-world constraints or interpret solutions visually for dispatchers).

4. Trust and usability matter

Drivers and dispatchers had to trust quantum-generated routes. Volkswagen developed a graphical interface with side-by-side comparisons of quantum and traditional route proposals to build confidence.

Challenges and Future Steps

Despite the pilot’s success, several open challenges remain:

• Scalability: QUBO problem sizes are still constrained by qubit count and connectivity.

• Noise and error rates: While annealers are more robust than gate-based systems, solution quality can degrade with complex constraints.

• Model complexity: Translating real-world rules into QUBO formulations remains a specialized skill.

• Integration hurdles: Logistics systems are often siloed. Integrating quantum decision engines into legacy route planners and TMS platforms requires middleware development.

Volkswagen is now working on a toolkit for QUBO modeling in logistics, aimed at fleet operators who lack quantum expertise but want to start experimentation.

Conclusion: A Quantum Step Forward for Urban Logistics

Volkswagen’s June 2021 pilot in Barcelona marked a watershed moment in the application of quantum computing to real-world logistics. By deploying quantum annealing to optimize dynamic last-mile deliveries, the company demonstrated that quantum computing can provide not just theoretical value but tangible operational gains today.

As urban environments become more congested and e-commerce demands surge, the ability to compute better routes, faster, and under tighter constraints is becoming a competitive necessity. Quantum tools—particularly in hybrid configurations—may be the next step in building greener, more efficient, and more resilient logistics systems.

With this pilot, Volkswagen has positioned itself as a first mover in quantum logistics optimization, offering a glimpse into how fleets, cities, and tech platforms will collaborate to create the quantum-enabled smart cities of tomorrow.

Why Cold Chain Logistics Presents a Unique Challenge

Cold chain logistics refers to the storage, handling, and transportation of goods that must be kept within a narrow temperature range throughout the supply chain. This is particularly critical for:

• Pharmaceuticals (e.g., vaccines, biologics, insulin)

• Perishables (e.g., seafood, dairy, fresh produce)

• Temperature-sensitive chemicals

These supply chains are exceptionally complex due to:

• Perishable time windows that require rapid and precise routing

• Multimodal transport coordination across air, sea, rail, and road

• Regulatory compliance with handling and storage standards

• High cost of failure, including product spoilage and safety risks

Traditional optimization software can manage many of these variables, but as scale, disruption, and demand volatility increase, the decision space grows exponentially. This is where quantum computing offers an edge.

The Classiq–Intel Quantum Pilot: Overview and Goals

The pilot project, launched in Q1 2021, was one of the first to apply quantum algorithm synthesis to cold chain logistics modeling. The key players:

• Classiq Technologies (Tel Aviv): Specializes in high-level quantum algorithm synthesis tools.

• Intel Quantum (U.S.): Develops superconducting quantum processors and hybrid architectures.

The partnership focused on two core objectives:

1. Leverage Classiq’s synthesis platform to rapidly design quantum circuits tailored to cold chain logistics optimization.

2. Benchmark these circuits on Intel’s quantum hardware simulators, evaluating feasibility, scalability, and future deployment potential.

The collaboration was also aligned with the goals of the Quantum Economic Development Consortium (QED-C), which both Intel and Classiq are active members of.

Targeted Use Case: mRNA Vaccine Cold Chain

To make the pilot concrete and socially relevant, the partners modeled a logistics scenario involving the global distribution of mRNA COVID-19 vaccines, which require cold or ultra-cold storage:

• Pfizer/BioNTech vaccines: require −70°C

• Moderna vaccines: require −20°C

• Sensitive expiration timeframes once thawed

The model included 5 major logistics hubs (distribution centers) and 30 last-mile delivery nodes (clinics, hospitals, regional storage). The goals:

• Optimize delivery routes and schedules under cold chain constraints

• Minimize spoiled units due to delays or misrouted shipments

• Increase resilience against transport disruptions (e.g., customs, weather)

• Balance carbon footprint and cost across multimodal options

Quantum Approach vs. Classical Optimization

Traditional Tools

Cold chain optimization typically uses:

• Mixed Integer Linear Programming (MILP)

• Heuristic search (genetic algorithms, tabu search)

• Monte Carlo simulation for uncertainty modeling

These work well at small scale but suffer from long runtimes and local optima traps in highly constrained environments.

Quantum Contribution

Classiq’s platform allowed engineers to automatically synthesize quantum circuits that encapsulate:

• Vehicle Routing Problem (VRP) under thermal constraints

• Dynamic resource allocation (e.g., dry ice packs, thermal containers)

• Time-window scheduling for multi-drop shipments

• Multivariate penalty scoring (e.g., late arrival, high cost, risk level)

Classiq’s abstraction layer made it possible to model these use cases at a high level, reducing the need for deep quantum programming expertise.

Implementation Highlights

Quantum Circuit Design

Using Classiq’s synthesis engine, the team generated customized quantum circuits implementing versions of:

• Quantum Approximate Optimization Algorithm (QAOA)

• Quantum Minimum Cost Flow algorithms

• Penalty encoding for temperature thresholds and delay risks

The circuits were designed for a 30-node logistics network with ~50 constraints.

Simulation and Benchmarking

The circuits were run on Intel’s quantum simulator stack, which included:

• High-fidelity noise models based on Intel’s Tangle Lake processor research

• Hybrid classical-quantum optimizers to solve QAOA parameterization

• Metrics tracking energy state convergence, constraint violation rates, and iteration efficiency

Classical solvers (e.g., CPLEX) were used as baselines for comparison.

Results and Key Findings

1. Feasibility of High-Constraint Quantum Models

The synthesized quantum circuits successfully encoded complex, real-world logistics constraints, including thermal decay curves, priority delivery tiers, and depot capacity rules.

2. Early Quantum Advantage in Solution Diversity

While classical solvers found a single best solution, quantum methods generated a distribution of high-quality solutions, offering better adaptability in dynamic environments.

This is crucial in cold chains, where a sudden failure (e.g., truck breakdown) requires fast rerouting based on pre-calculated alternatives.

3. Noisy Device Limitations Still Apply

Real-hardware readiness remains years away. Quantum simulators showed potential, but real QPUs still struggle with circuit depth and noise fidelity at this complexity.

4. Time-to-Solution Tradeoffs Favor Hybrid Models

Quantum-enhanced models found good solutions faster than classical-only tools in limited scenarios. However, best results were achieved using hybrid solvers, where quantum engines handled route encoding and constraint balancing while classical layers handled cost evaluations.

Industry Implications and Strategic Takeaways

This pilot illustrates several broader trends in the convergence of quantum and logistics:

• Domain-specific quantum synthesis is key: Classiq’s ability to abstract away circuit details allowed logistics experts to engage directly.

• Hybridization is the near-term path: Quantum won’t replace classical, but will augment it in high-complexity subroutines.

• Cold chain is ripe for disruption: The perishability factor creates optimization pressure, making it an ideal testbed for emerging technologies.

• Quantum readiness starts now: Early pilots like this build internal knowledge and data formats that can scale later with better hardware.

Next Steps and Roadmap

Following the success of the June 2021 pilot, Classiq and Intel outlined the following actions:

• Develop a GUI-driven toolkit for quantum supply chain modeling aimed at pharma clients

• Extend the model to air cargo integration, including dynamic pricing and regulatory delays

• Explore integration with logistics IoT sensors, allowing quantum models to react to real-time cold chain events (e.g., temperature breaches)

• Partner with vaccine manufacturers and logistics companies (e.g., DHL, UPS Healthcare, Maersk) for deeper vertical integration

Challenges and Open Questions

Despite the promise, the pilot also highlighted key limitations:

• Quantum circuit interpretability: Business teams still struggle to trust “black box” quantum outputs.

• Hardware timelines: Intel’s superconducting devices remain in lab-testing phases. Deployment-grade systems are several years out.

• Integration barriers: Cold chain logistics systems (TMS, WMS, ERP) are not yet designed to plug into quantum engines.

Intel is addressing some of these via its quantum software SDK and collaboration with classical cloud players like AWS and Azure Quantum.

Conclusion: Preparing the Cold Chain for a Quantum Future

The Classiq–Intel pilot marks a significant milestone in quantum logistics, not just for what it achieved technically, but for what it signaled strategically: that the cold chain industry is actively seeking innovation to meet growing complexity and fragility.

By modeling real-world vaccine delivery networks under tight thermal constraints and deploying cutting-edge quantum synthesis tools, the project showed that quantum computing can bring near-term advantages—even in today’s limited hardware environment—through intelligent hybridization and problem-specific modeling.

As the world prepares for more resilient and data-driven supply chains, quantum technology is set to play a foundational role in shaping how perishable goods move across the globe.

Why Aerospace Logistics Is a Prime Quantum Candidate

Aerospace supply chains are among the most intricate and high-stakes logistics networks globally. With aircraft production involving tens of thousands of parts sourced across continents—and strict constraints on timing, compliance, and safety—efficiency is paramount.

Airbus, like its competitors, operates a “just-in-sequence” logistics model across its global network of final assembly lines (FALs) in Toulouse, Hamburg, Tianjin, and Mobile. The COVID-19 pandemic, combined with geopolitical and trade disruptions, exposed vulnerabilities in this model.

Airbus’s Advanced Analytics and AI division began assessing how emerging quantum computing capabilities might assist with:

• Inventory rebalancing of aerospace-grade parts across distributed warehouses

• Aircraft cargo load optimization for variable-demand delivery scenarios

• Routing spare parts shipments from suppliers to production hubs under complex constraints

• Resilience planning in case of border disruptions, delays, or supplier failures

These problems, typically modeled using mixed-integer programming (MIP), often strain classical solvers at scale—especially when real-time planning is required.

Inside the Airbus–QC Ware Quantum Logistics Project

Airbus selected QC Ware for this pilot based on the startup’s focus on hardware-agnostic quantum software and its proven track record in finance and manufacturing optimization. The collaboration centered around Forge, QC Ware’s flagship platform for hybrid quantum-classical algorithm development.

The joint study was structured in three phases:

Phase 1: Problem Formalization

Airbus’s operations teams provided anonymized data models from two logistics cases:

1. Aircraft Cargo Reconfiguration: Repacking and weight balancing for A350 air freight missions during COVID-era repurposing.

2. Global Spare Parts Logistics: Optimizing routes for the movement of high-value aircraft parts between production sites and maintenance depots.

Each case involved over a dozen constraints and required multi-objective optimization (e.g., time vs. cost vs. load balance).

Phase 2: Algorithm Mapping

QC Ware’s quantum scientists translated the logistics problems into quantum-friendly formulations such as:

• Quadratic Unconstrained Binary Optimization (QUBO)

• Constrained Variational Quantum Eigensolvers (VQE)

• Quantum Approximate Optimization Algorithms (QAOA)

These were implemented on Forge and simulated across hardware backends from IBM Q, IonQ, and D-Wave, offering a view into different quantum modalities.

Phase 3: Benchmarking

QC Ware ran hybrid optimization benchmarks comparing quantum solvers to traditional MIP tools like Gurobi and SCIP. Metrics included:

• Solution optimality

• Time-to-solution

• Computational resource usage

• Sensitivity to constraint perturbation

Key Outcomes and Findings

By mid-June 2021, Airbus and QC Ware completed the simulation runs and released a joint white paper (internal circulation at Airbus). Highlights included:

1. Quantum Edge in Small-Scale Logistics Planning

For cargo loading problems involving <200 items with dynamic constraints (e.g., weather-driven route changes, urgency flags), hybrid quantum solvers found comparable or better solutions up to 20% faster than classical methods.

2. Superior Adaptability

Variational quantum methods showed robustness in adapting to constraint perturbations, which are common in real-world aerospace logistics (e.g., sudden customs hold, aircraft unavailability).

3. Noisy Intermediate-Scale Quantum (NISQ) Limitations

Hardware tests revealed that while simulators performed well, current quantum processors still suffered from gate noise and depth constraints, limiting their practical advantage to toy-sized models.

4. Strategic Insight for Scaling

Airbus concluded that while production deployment may take 3–5 years, early engagement is vital to build internal expertise and prepare models for future-scale quantum processing.

Applications Across Airbus Operations

This project mapped quantum solutions to multiple future use cases within Airbus and its logistics partners:

• AOG (Aircraft on Ground) Support: Routing emergency spare parts via multimodal transport (truck, air, rail).

• Manufacturing Sequencing: Optimizing parts delivery sequences for aircraft final assembly.

• Sustainable Logistics Planning: Minimizing emissions across complex logistics corridors.

• Autonomous Vehicle Coordination: Applying quantum route optimization to warehouse robots and AGVs.

Airbus also hinted at plans to integrate quantum-enhanced forecasting into Skywise, its open digital aviation platform used by hundreds of airlines and MRO providers worldwide.

QC Ware’s Strategic Role and Platform Advantage

QC Ware positioned itself not just as a service provider but as a platform partner, emphasizing the importance of hardware-agnosticism. With Forge, Airbus was able to:

• Test across quantum backends (e.g., superconducting, trapped ion, annealing)

• Run hybrid workflows combining GPUs and QPUs

• Use Python APIs familiar to Airbus data science teams

• Conduct secure benchmarking without vendor lock-in

Forge also supported algorithm customization, allowing Airbus engineers to test tradeoffs between execution time and solution optimality — a key requirement for logistics planners under tight SLAs.

Broader Ecosystem and Industry Context

This announcement came amidst a flurry of aerospace interest in quantum computing:

• Lockheed Martin had partnered with D-Wave on supply chain simulations.

• Boeing was exploring quantum-based materials design.

• NASA Ames had conducted quantum scheduling research for air traffic management.

• Rolls-Royce joined the UK’s NQCC in early-stage quantum propulsion R&D.

Europe’s Quantum Flagship and the French National Quantum Plan also identified aerospace and logistics as strategic sectors for applied quantum pilot programs.

This ecosystem provided Airbus with access to funding, academia (notably INRIA and CNRS), and technology validation partners.

Challenges Ahead

While the Airbus–QC Ware collaboration was successful as a research project, both parties outlined limitations and areas needing development:

• Scalability: Current quantum devices cannot yet handle full-scale Airbus logistics models.

• Interpretability: Solutions from QAOA and VQE require new tools for human-understandable outputs.

• Integration: Connecting quantum solvers to existing ERP and TMS systems remains a hurdle.

• Skills Gap: Airbus needed to upskill logistics and operations researchers in quantum concepts.

To address these, Airbus began internal training programs and initiated exploratory talks with SAP and IBM for middleware integration pathways.

What Comes Next

Encouraged by the June 2021 results, Airbus and QC Ware outlined next steps for the second half of the year:

• Model reverse logistics flows for maintenance and recycling

• Launch a quantum logistics sandbox for Airbus supply chain teams

• Expand problem sets to include multi-echelon inventory optimization

• Co-author a public paper for the Quantum for Logistics 2022 forum

Airbus also proposed engaging airlines in the project, particularly for joint cargo route planning and carbon footprint modeling.

Conclusion: Building Quantum Readiness in Aerospace Supply Chains

The Airbus–QC Ware project marked one of the first public attempts to apply quantum computing to complex aerospace logistics. While practical quantum advantage remains years away, this collaboration planted critical seeds:

• Logistics models pre-optimized for quantum

• Teams trained on hybrid algorithm design

• Benchmarks to measure future hardware gains

• Strategic alignment with European quantum policy

For an industry where delays cost millions and supply chain agility determines competitiveness, even marginal gains can be transformative. This project helped Airbus begin its quantum journey — not in theory, but in practice.

Why Volkswagen Chose Quantum Now

As global cities densify and e-commerce continues its rise, logistics has become a key challenge for automakers evolving into mobility providers. Volkswagen has long invested in AI, autonomous vehicles, and smart city platforms. But by mid-2021, the automaker began facing diminishing returns in complex route planning using conventional computing, especially under real-time constraints in urban environments like Toronto, São Paulo, and Berlin.

Volkswagen’s internal simulations showed that existing algorithms struggled with multi-variable constraints such as:

• Real-time traffic variability

• Limited delivery windows

• Driver hour regulations

• Emissions-based zone restrictions

• Mixed vehicle fleet configurations

By partnering with Xanadu, a leader in photonic quantum computing, VW aimed to test whether quantum-native methods could provide superior solutions in these high-complexity scenarios.

Inside the VW–Xanadu Quantum Logistics Initiative

The joint research project launched a dedicated Quantum Logistics Simulation Lab in downtown Toronto, co-located with Xanadu’s headquarters. The mission: to develop and test quantum algorithms that could eventually run on Xanadu’s Borealis photonic quantum processors and be deployed in VW’s logistics stack.

Key research themes:

• Quantum Variational Optimization for multi-stop delivery planning

• Hybrid Classical–Quantum Scheduling for last-mile fulfillment

• Monte Carlo Quantum Sampling for traffic condition modeling

• Constraint-based Vehicle Assignment using quantum-enhanced solvers

These research modules were coded in PennyLane, Xanadu’s open-source software library that supports quantum differentiable programming — a natural fit for AI/logistics applications.

Technical Architecture:

1. Data Ingestion: VW supplied anonymized delivery datasets from its operations in North America and Europe.

2. Simulator Layer: The team built a quantum-capable logistics simulator that could ingest real-world maps and constraints.

3. Quantum Backend: Optimization modules were prototyped both on Xanadu’s simulators and Borealis photonic chips.

4. Benchmark Layer: All outputs were benchmarked against classical solvers (e.g., OR-Tools, CPLEX, Gurobi).

Early Outcomes: Modest Gains, Big Promise

By late June 2021, the VW-Xanadu team had completed its first round of tests on synthetic data sets simulating Toronto's downtown delivery corridors. Key findings included:

• 5–9% improvement in total route cost for constrained deliveries

• Better adaptability to traffic disturbances when using variational quantum approaches

• More balanced load distribution across mixed vehicle fleets

• Feasibility of photonic hardware for early-stage real-world logistics problems

Although still early-stage, these results were enough to warrant additional investment in joint R&D for the second half of 2021.

Xanadu’s Photonic Edge

Xanadu’s core value in the project stemmed from its photonic quantum computing platform. Unlike superconducting or trapped-ion architectures, photonic systems use light particles (photons) to encode quantum information. This has several advantages in logistics applications:

• Room-temperature operation, enabling potential integration into edge logistics devices in the future

• High-speed optical data flow, important for fast simulation and feedback

• Energy efficiency, key for sustainability targets in urban operations

• Scalable entanglement networks, ideal for multi-objective optimization

Their Borealis quantum computer, unveiled around this time, achieved 216-mode Gaussian boson sampling — the most complex photonic quantum operation demonstrated as of mid-2021 — showing promise for future logistics algorithms.

Strategic Implications for Automotive Logistics

Volkswagen's investment in quantum logistics is more than experimental. It aligns with broader ambitions in:

• Urban Mobility Services: Optimizing vehicle and package flows in smart cities

• EV Logistics: Coordinating charging and routing in energy-constrained environments

• Fleet-as-a-Service Models: Assigning delivery tasks across mixed fleets (e.g., bikes, EV vans, autonomous pods)

• Sustainability Metrics: Using quantum optimization to reduce route emissions

By collaborating early with a quantum startup like Xanadu, VW positioned itself as a first mover in the quantum supply chain space — a space that could define competitive advantage by 2030.

Broader Industry and Ecosystem Context

This project came amid growing quantum interest from the automotive and logistics sectors:

• Daimler was working with IBM on quantum battery simulation.

• Toyota began exploring quantum risk analysis in mobility.

• DHL and Terra Quantum were starting to pilot hybrid quantum routing (later in 2022).

• Maersk initiated early quantum scheduling discussions with Rigetti.

Meanwhile, Canada — home to Xanadu — had emerged as a quantum leader through:

• The Pan-Canadian Quantum Strategy (preliminary programs active by 2021)

• Major hubs like University of Waterloo’s IQC, Toronto’s Creative Destruction Lab – Quantum, and Montreal’s Mila

This made Canada an ideal launchpad for global quantum logistics R&D.

Challenges Identified

Despite progress, VW and Xanadu noted several key challenges in their June 2021 update:

• Problem encoding: Translating real-world logistics constraints into quantum-ready formats (like QUBO or CVaR) required heavy reengineering.

• Hardware noise: Photonic chips, while promising, still struggled with readout errors under high-mode sampling.

• Talent bottlenecks: Bridging logistics engineering with quantum algorithm development required new interdisciplinary roles.

• Deployment gap: Even promising quantum solutions remained in simulation or lab settings — real-time logistics deployments were still 1–2 years out.

To address these, the partners launched a talent development program with the University of Toronto and announced a roadmap for edge-device simulations.

Looking Ahead: What Comes After June 2021

As the second half of 2021 approached, VW and Xanadu outlined next steps:

• Integrate AI and quantum routing for joint optimization

• Simulate peak holiday delivery scenarios using hybrid solvers

• Expand beyond Toronto to model cities in Germany and Brazil

• Publish an open-access white paper on quantum logistics architecture (expected late 2021)

They also discussed forming a consortium to bring other logistics partners and city planners into the effort — building momentum toward what they called a “Quantum Urban Mobility Stack.”

Conclusion: The First Glimpse of Quantum Delivery Networks

This June 2021 initiative was among the first to explore quantum-native logistics optimization at an enterprise level, combining automotive, photonic quantum hardware, and last-mile delivery simulation.

It marked a shift from abstract quantum promise to applied logistics innovation, with implications for:

• Sustainable urban operations

• Advanced vehicle coordination

• National quantum strategy alignment

• Competitive advantage in supply chain design

As Volkswagen and Xanadu advanced their collaboration, they offered a glimpse of a future where delivery networks are not just digitized — they’re quantum-optimized.

The Security Bottleneck in Global Supply Chains

Modern global logistics is driven by data as much as physical goods. Every shipment across a border is accompanied by a digital trail—customs declarations, bills of lading, origin certificates, real-time location updates, and dynamic routing decisions based on demand forecasts.

These exchanges—between freight forwarders, customs authorities, manufacturers, and carriers—are vulnerable to cyberattacks and data tampering. The 2020 SolarWinds hack and the 2021 Colonial Pipeline ransomware attack highlighted the fragility of digital supply chains.

Recognizing this vulnerability, in May 2021, the EU-supported “EuroQCI Logistics Pilot” was launched to explore quantum key distribution (QKD) as a method to future-proof logistics communication against cyber threats, including those posed by quantum computers.

What Is QKD and Why It Matters for Logistics

Quantum key distribution leverages principles of quantum mechanics—such as the no-cloning theorem and entanglement—to securely transmit encryption keys. The key advantage of QKD over classical key exchange methods is that any attempt at interception disrupts the quantum state, alerting users to potential eavesdropping.

For logistics operations that depend on trusted information (e.g., the correct sequencing of just-in-time delivery instructions, or authenticity of customs forms), QKD provides:

• Tamper-evident data exchange.

• Protection against future quantum decryption threats.

• Enhanced trust in cross-border data integrity.

In a sector where cargo flow often determines production continuity, ensuring the security of data that governs those flows is paramount.

The Consortium and Its Structure

The pilot was led by Deutsche Telekom, in collaboration with:

• Atos – providing cybersecurity and quantum communication interfaces.

• TNO (Netherlands Organization for Applied Scientific Research) – managing logistics integration at Dutch ports and hubs.

• French National Cybersecurity Agency (ANSSI) – advising on cryptographic compliance and resilience.

• DB Schenker and Kühne + Nagel – logistics partners offering operational testbeds.

• European Commission’s DG MOVE – supervising alignment with trans-European transport and cybersecurity policy.

These partners aimed to build a three-node QKD network between logistics centers in Hamburg (Germany), Rotterdam (Netherlands), and Lille (France), representing high-volume corridors for multimodal freight.

Targeted Use Cases for Quantum-Secured Logistics

Unlike academic experiments, this pilot focused on real-world, high-priority logistics data workflows, including:

1. Customs Pre-Clearance Documents

Before goods arrive at a border checkpoint, digital declarations and risk assessments are exchanged. The pilot tested securing these transmissions with QKD between customs servers and freight forwarding systems to prevent manipulation or premature data leakage.

2. Carrier Booking Updates

When trucks or containers are rerouted due to traffic or port congestion, updated booking data must be transmitted between shippers, carriers, and terminals. Ensuring these updates are not intercepted or spoofed is vital to avoid misdeliveries.

3. Factory Line Instructions

For just-in-time automotive manufacturing, factories in France and Germany send part assembly instructions to suppliers in the Netherlands in real time. Quantum encryption was tested to protect the integrity of these manufacturing control signals.

Technology Stack: From Quantum Photons to APIs

The QKD infrastructure relied on fiber-based entangled photon pair distribution, with secure quantum channels spanning distances up to 150 km between trusted nodes.

Key technology components included:

• Quantum key management appliances developed by ID Quantique and installed at each node.

• Key negotiation protocols integrated with existing IPsec/VPN stacks to provide seamless encryption for freight management software.

• Custom APIs to plug QKD-derived keys into EDI (Electronic Data Interchange) platforms used by logistics firms.

This hybrid approach allowed QKD to serve as a drop-in security upgrade without requiring changes to end-user software.

Early Results and Feasibility Outcomes

By the end of May 2021, the consortium reported successful tests across all three legs of the trial network:

• Average QKD key refresh rate exceeded 10 kbps, sufficient for securing logistics metadata and document encryption.

• No significant latency overhead was observed, even during high-load simulation scenarios with up to 5,000 document exchanges per hour.

• Live alerting was triggered during intentional eavesdropping simulations, validating the system’s quantum-based intrusion detection.

In one key test, a logistics scheduling conflict was intentionally created between Hamburg and Rotterdam by modifying an EDI message mid-transmission. The QKD-secured version was flagged immediately, while a parallel non-secure channel failed to detect the intrusion.

Strategic Alignment with European Quantum Initiatives

This pilot was a flagship demonstration aligned with:

• EuroQCI (European Quantum Communication Infrastructure): The EU's long-term vision to create a pan-European secure quantum network.

• GAIA-X: Ensuring data sovereignty in European cloud and supply chain systems.

• NIS2 Directive: Upcoming regulations on cybersecurity for essential services, including transportation and logistics.

By deploying QKD in an operational logistics environment, the pilot set a precedent for integrating post-quantum resilience into EU supply chain infrastructure.

Lessons Learned and Remaining Gaps

While the pilot showed the feasibility of QKD in logistics, several practical challenges emerged:

1. Physical Infrastructure Limits

QKD over fiber is range-limited (~100–200 km without trusted repeaters). Long-haul logistics corridors will require either satellite-based QKD or quantum repeaters—both in early development.

2. Cost of Deployment

QKD systems remain expensive, especially for small and medium logistics firms. Future integration may depend on centralized infrastructure models (e.g., telecom-provided QKD-as-a-Service).

3. Regulatory Ambiguity

Quantum encryption is not yet explicitly recognized in all customs and data protection frameworks. Harmonization will be essential for global rollout.

4. Human Trust and Change Management

While quantum cryptography provides mathematically provable security, logistics staff and IT managers must trust and understand the system. Training and intuitive dashboards will be needed for adoption.

Roadmap: Toward a Quantum-Secured Supply Chain

Following the pilot, the consortium outlined a multi-year roadmap:

• 2022–2024: Expand QKD coverage to Antwerp, Milan, and Warsaw; test integration with 5G-connected IoT logistics devices.

• 2024–2026: Link QKD with quantum random number generation (QRNG) for high-entropy logistics authentication tokens.

• Post-2026: Tie into quantum satellite relay systems such as those being developed by ESA and Singapore, enabling global QKD beyond Europe.

Conclusion: A Step Toward Post-Quantum Trade Resilience

The May 2021 cross-border QKD pilot showed that quantum-secure communication is not just a theoretical future but a deployable technology capable of strengthening one of the most vulnerable aspects of modern trade—logistics data security.

As quantum computing threatens to break traditional encryption, supply chain actors must rethink the foundations of digital trust. This project offers a template for how quantum communication and classical logistics can work hand-in-hand, setting a global benchmark for post-quantum readiness in international commerce.

Bringing Quantum to the Factory Floor

Automotive manufacturing facilities are sprawling, intricately timed ecosystems where materials, parts, and components must move with near-zero error. Inside BMW’s smart factories, autonomous mobile robots (AMRs) are increasingly replacing traditional conveyors and forklifts for intra-logistics—delivering components from storage to assembly lines.

In May 2021, the BMW Group took a forward-looking step by announcing a collaborative quantum pilot with Honeywell Quantum Solutions, with the goal of evaluating whether quantum computing could outperform classical route planners in complex, congested factory environments.

Unlike last-mile delivery on open roads, factory logistics introduces a different set of optimization challenges: closed environments, tight timing windows, shared corridors, and unpredictable pauses due to human workers and machine recalibrations. Traditional algorithms can bottleneck under such dynamic constraints.

The Core Challenge: Multi-Agent Path Finding Under Constraints

BMW’s AMRs must constantly navigate between different zones (e.g., storage, quality control, assembly bays) while:

• Avoiding collisions with each other and human workers.

• Adhering to strict time windows to synchronize with the production cycle.

• Reacting in real time to temporary obstacles and changes in route availability.

This class of problem is known as Multi-Agent Path Finding (MAPF), an NP-hard combinatorial optimization challenge. As the number of agents increases, the search space grows exponentially, making it difficult for classical methods to guarantee optimal or near-optimal solutions quickly.

Honeywell Quantum Solutions proposed applying quantum-enhanced optimization algorithms to this MAPF problem using its trapped-ion quantum hardware—among the most coherent and precise quantum processors available at the time.

Quantum Computing in Practice: The QCCD Model

The Honeywell quantum processor was based on the quantum charge-coupled device (QCCD) architecture, leveraging trapped ytterbium ions held in electromagnetic fields. These ions act as qubits, and Honeywell’s system was known for high fidelity, low error rates, and tunable connectivity.

For BMW’s pilot, the companies developed a custom quantum workflow with the following components:

• Problem encoding: BMW’s route planning data was transformed into a Quadratic Unconstrained Binary Optimization (QUBO) model.

• Quantum optimization: Algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and variational techniques were applied.

• Hybrid feedback: Outputs from quantum runs were combined with classical heuristics to refine route recommendations and timing sequences.

Because the scale of today’s quantum processors is limited, BMW and Honeywell used a hybrid simulation approach to test quantum feasibility on scaled-down yet realistic factory scenarios.

Real-World Testbed: Regensburg Plant Simulation

The quantum pilot was anchored in a virtual simulation of the BMW Regensburg Plant, one of the company’s leading smart factories in Germany. The simulation incorporated:

• Detailed digital twin models of warehouse layouts and vehicle paths.

• Live task scheduling data from production management systems.

• Simulated AMR telemetry and motion control parameters.

Within this digital twin, BMW evaluated how quickly and accurately quantum-enhanced solvers could re-route mobile robots in response to unexpected traffic and delay events.

Results: Promise in Congested Routing and Task Preemption

The pilot yielded encouraging preliminary results:

• Up to 14% improvement in average task completion time for AMRs during high-congestion periods, compared to classical A* and greedy solvers.

• Higher success rates in scenarios with simultaneous task changes (e.g., last-minute rerouting due to human presence in a corridor).

• Reduced computational overhead in exploring alternate paths under changing constraints, with quantum-enhanced solvers generating more diverse route options.

Interestingly, even when quantum processors were simulated (due to qubit limitations), the quantum-inspired heuristics showed strong generalization and robustness, suggesting utility well before full-scale quantum advantage is reached.

Human-Machine Collaboration and Interpretability

One of the central concerns raised by BMW’s factory engineers was the transparency of quantum outputs. In high-stakes production environments, decisions must be explainable to human supervisors.

To address this, Honeywell developed visual overlays of the quantum-derived routes, showing confidence intervals and alternate path options, enabling supervisors to:

• Compare quantum paths against classical ones.

• Identify potential collisions or delay points in advance.

• Understand trade-offs in distance vs. timing vs. safety margins.

These interpretability layers were key in building trust among production engineers and logistics coordinators.

Strategic Implications: BMW’s Quantum Horizon

This project reflects BMW’s broader commitment to becoming a leader in quantum readiness. In addition to logistics optimization, BMW had previously announced:

• Participation in the PlanQK project (Platform and Ecosystem for Quantum Applications) in Germany.

• Quantum chemistry modeling partnerships for battery development.

• Quantum benchmarking initiatives with Pasqal and the Fraunhofer-Gesellschaft.

With logistics being one of the most operationally mature areas for quantum applications, the May 2021 pilot with Honeywell offered a practical proving ground for real-time quantum-human collaboration.

Honeywell’s Strategic Positioning and Evolution

Honeywell Quantum Solutions, shortly after this pilot, merged with Cambridge Quantum Computing to form Quantinuum—a full-stack quantum company.

This BMW project helped position Honeywell/Quantinuum as not only a hardware provider but a partner in end-to-end quantum logistics solutioning, capable of integrating real-time systems, digital twins, and interpretable quantum workflows.

The lessons learned were later applied in collaborations with DHL and other industrial logistics players.

Sector-Wide Impact and Future Outlook

The pilot exemplified a shift in how manufacturers approach logistics planning. Rather than treating quantum as a distant possibility, BMW’s approach emphasized:

• Hybrid implementation now using existing QPU and classical infrastructure.

• Use of digital twins as quantum testbeds.

• Focus on specific, bounded use cases (like AMR traffic flow) rather than monolithic supply chain optimization.

Going forward, BMW plans to expand these experiments into:

• Multi-plant coordination, where inter-factory shipments and workflows can be quantum-optimized.

• Energy-aware routing, combining quantum optimization with carbon footprint reduction metrics.

• Real hardware integration, as QPU capacity scales up in the coming years.

Conclusion: Quantum Pathfinding, One Factory at a Time

This May 2021 initiative marked a pioneering moment in quantum logistics for the automotive sector. By tackling real-world problems like AMR path planning with cutting-edge quantum methods, BMW and Honeywell demonstrated that quantum advantage may emerge not in abstract theory, but on the factory floor.

As automotive manufacturing becomes more autonomous and modular, quantum-enhanced intra-logistics could be a key lever for future-ready, resilient production systems.

From Robotics to Quantum: Amazon’s Expanding Logistics Toolbox

Amazon has long dominated global logistics through scale, speed, and technology. The company pioneered warehouse automation through Kiva Systems (now Amazon Robotics), and its Prime delivery model set industry benchmarks for fulfillment.

In May 2021, Amazon Robotics quietly expanded its R&D horizon by entering into a pilot project with Zapata Computing, a leading quantum software developer, to explore how quantum-inspired optimization could further accelerate Amazon’s robotics performance—especially for dense last-mile delivery scenarios in urban and suburban environments.

While Amazon has traditionally leaned on classical AI and heuristic solvers for logistics decisions, the increasing computational demands of route planning, picker tasking, and dynamic bin packing pushed their R&D teams to consider alternative methods—quantum computing being the boldest frontier.

The Quantum Problem: NP-Hard Meets Same-Day Shipping

Last-mile delivery represents the most complex and costly segment of the logistics chain—accounting for up to 53% of total shipping costs, according to industry data. Core challenges include:

• Route compression under real-time traffic, weather, and delivery constraints.

• Dynamic bin packing for vans, lockers, and mobile storage bots.

• Real-time task allocation for mobile pick-and-pack units inside robotic fulfillment centers.

Each of these problems belongs to the NP-hard family—where the number of possible combinations grows exponentially with task size. Classical computing can only go so far in solving these in time-sensitive contexts.

That’s where Zapata’s quantum-inspired algorithms came in.

Zapata’s Role: Hybrid Quantum Algorithms for Supply Chain Tactics

Boston-based Zapata Computing specializes in Orquestra®, a software platform for building, orchestrating, and executing quantum workflows. While early quantum hardware still faces scale limitations, Zapata’s focus has been on hybrid quantum-classical algorithms that can run on simulators and near-term quantum processors—particularly in optimization and machine learning tasks.

For Amazon Robotics, Zapata delivered custom versions of:

• Variational Quantum Eigensolvers (VQE) adapted for routing compression.

• Quantum Approximate Optimization Algorithm (QAOA) variants for bin-packing decisions.

• Dynamic assignment solvers for worker and robot task scheduling using tensor networks.

These algorithms were run initially on simulated environments using real delivery and routing data from Amazon warehouses in North America and Europe.

Experimental Setup and Key Objectives

The pilot had three core experimental goals:

1. Validate feasibility of quantum-inspired algorithms on real-world logistics problems.

2. Benchmark performance against Amazon’s internal heuristics and ML models.

3. Determine hybrid workflows that could integrate with AWS and Amazon Robotics middleware.

Amazon Robotics engineers worked closely with Zapata’s quantum scientists to define cost functions, constraints, and system parameters. Notably, data from mobile fulfillment centers and Amazon Scout (the autonomous sidewalk delivery robot) were used to simulate dynamic task environments.

Preliminary Results: Early Wins in Complex Environments

While the results were exploratory, they revealed promising gains in specific domains:

• 8–11% reduction in picker travel time within dynamic warehouse zones using quantum-influenced task allocation.

• Up to 9% improvement in bin-packing efficiency for suburban delivery vans, reducing fuel usage and trip count.

• Higher adaptability to real-time routing disruptions compared to classical solvers, particularly in high-density delivery scenarios with over 250 stops.

The success hinged less on quantum supremacy and more on better combinatorial optimization heuristics, inspired by quantum techniques. These hybrid solvers offered more robust outputs under variable constraints than some deep-learning models alone.

Integration with Amazon Web Services (AWS)

A crucial aspect of the project involved testing how quantum workflows could plug into Amazon’s cloud infrastructure. Zapata built and ran Orquestra workflows on AWS, allowing Amazon Robotics teams to:

• Deploy quantum simulations on cloud-based high-performance computing (HPC) clusters.

• Use Zapata’s orchestration layer to manage hybrid optimization pipelines alongside existing ML-based route planners.

• Monitor performance in real time through dashboards integrated with Amazon Robotics’ internal logistics observability systems.

This validated early-stage deployment feasibility, especially for “quantum-in-the-loop” systems where classical and quantum engines work in tandem to improve decision accuracy and resilience.

Strategic Fit: Scaling Quantum within Amazon’s Logistics Vision

The Amazon–Zapata partnership aligned with broader Amazon initiatives across its logistics ecosystem, including:

• The Amazon Scout robot program, where micro-routing under energy constraints is key.

• Prime Air drone delivery, which demands quantum-like pathfinding under 3D airspace constraints.

• Amazon Flex, where crowdsourced drivers face routing and bin-loading problems ideal for quantum optimization.

• The Just Walk Out retail model, where real-time product tracking and replenishment could benefit from quantum-enhanced stock forecasting.

Quantum algorithms offer a pathway to boost efficiency while handling growing logistical complexity in real time.

Sectoral Implications: Retail Logistics Embraces Quantum Edge

Amazon wasn’t alone in its quantum experimentation. Around the same time:

• Walmart began exploring quantum inventory simulations with QC Ware.

• JD.com launched a pilot with Baidu to explore quantum optimization in last-mile delivery routes in urban Beijing.

• Alibaba Cloud partnered with Chinese quantum labs to evaluate warehouse layout optimization using quantum annealing.

Together, these moves signaled a shift: large-scale retail logistics providers were beginning to incorporate quantum concepts not just in R&D, but in operational design.

Limitations and Next Steps

Despite the promising pilot, Amazon identified several challenges:

• Data formatting overhead: Translating logistics variables into QUBO (Quadratic Unconstrained Binary Optimization) formats required significant preprocessing.

• Model interpretability: Engineers initially struggled to understand and validate quantum outputs.

• Hardware constraints: True quantum advantage wasn’t achieved due to limitations in current quantum processors.

To address these, Amazon Robotics began working on:

• Internal training programs for logistics engineers on quantum algorithm basics.

• Development of graphical debugging tools for visualizing quantum workflows.

• Further testing on new quantum hardware as it becomes available via AWS Braket.

Looking Ahead: From Pilot to Platform

Amazon is now assessing how to generalize these early wins into a broader platform strategy. Discussions within the Amazon Robotics and AWS Braket teams include:

• Expanding Zapata’s algorithms into micro-fulfillment centers globally.

• Bundling quantum-enhanced optimization as a feature within Amazon’s warehouse management systems.

• Collaborating with drone logistics teams to co-optimize airspace and delivery payloads.

By treating quantum optimization as a tool to augment—not replace—AI and classical models, Amazon is carving a path toward quantum-cooperative logistics.

Conclusion: Logistics as the Quantum Frontier

This May 2021 initiative underscores a vital truth: logistics is becoming one of the first commercial domains to test quantum algorithms at scale. Amazon’s early work with Zapata Computing points toward a hybrid future—where quantum optimization augments AI and real-time operations in the most demanding parts of the supply chain.

As logistics firms race to deliver faster, cheaper, and more sustainably, quantum techniques could be the edge that sets leaders apart in the decade ahead.

A New Intersection: Quantum Meets Warehouse Robotics

The global logistics sector has seen a major surge in autonomous mobile robots (AMRs) for e-commerce fulfillment and just-in-time inventory management. However, as these robots multiply, so do challenges in traffic coordination, route collisions, and idle time—especially in dense warehouse environments.

In May 2021, ColdQuanta and Zebra Robotics launched a joint initiative to address these problems using quantum-enabled coordination frameworks. Their objective: to integrate quantum sensors and optimization solvers into robotic fleet management systems, and develop a scalable, next-generation logistics stack for the warehouse of the future.

Key Objectives of the Pathfinder Program

The research pathfinder, supported in part by funding from the Colorado Office of Economic Development and International Trade (OEDIT), was designed to validate the following:

• Feasibility of quantum-enhanced location tracking for AMRs using ColdQuanta’s ultracold atom sensors

• Development of quantum-inspired optimization algorithms for dynamic path planning and congestion avoidance

• Integration with warehouse management systems (WMS) to enable full-stack logistics automation

While ColdQuanta brought deep physics expertise in quantum matter and sensing, Zebra Robotics contributed its proprietary warehouse navigation platform and multi-robot simulation toolkit.

Quantum Sensing in a Robotic Warehouse

At the core of the initiative was a proof-of-concept test using cold atom interferometry for precise AMR localization.

Unlike traditional GPS or RFID-based localization, ColdQuanta’s quantum sensors utilize ultracold atoms in vacuum chambers to measure inertial changes at nanometer-scale precision. These sensors were mounted on Zebra Robotics' AMRs to enable:

• High-resolution dead reckoning in environments where WiFi/GPS signals are weak

• Real-time velocity and tilt corrections during tight-path maneuvers

• Low-drift trajectory correction, allowing longer AMR operation cycles between recalibrations

This is particularly useful in logistics centers with narrow aisles and multi-tier racking systems, where traditional LiDAR or vision systems sometimes struggle with occlusions and reflections.

Quantum-Inspired Routing and Collision Avoidance

The second major technical milestone involved building quantum-inspired optimization modules for route planning.

ColdQuanta’s software team leveraged tensor network solvers and quantum annealing simulations to design algorithms that could:

• Dynamically re-route AMRs in real time to avoid bottlenecks

• Minimize idle time near charging docks or workstations

• Optimize multi-robot flow coordination across high-density warehouse zones

These optimization engines were implemented in Zebra Robotics’ simulation environment before being tested in a live warehouse pilot covering 2,000 square meters with 14 active AMRs.

Early Pilot Metrics (May–June 2021):

• 18% improvement in average AMR delivery cycle time

• 26% reduction in collision or conflict re-routing events

• Over 12 hours of autonomous runtime per unit without external recalibration

ColdQuanta’s Modular Quantum Stack

Founded in 2007, ColdQuanta’s expertise lies in cold atom physics—one of the key approaches in quantum sensing and computing. Their commercial platform, called “Hilbert,” includes:

• Quantum sensors for navigation and gravimetry

• Quantum matter systems for timing and metrology

• A roadmap toward neutral atom quantum processors

The company’s longer-term goal is to integrate quantum computation into logistics workflows—not just for optimization, but also for supply chain simulation, digital twin validation, and materials routing across global networks.

The Strategic Fit: Zebra Robotics’ AMR Platform

Zebra Robotics, a Denver-based startup spun off from a university research lab, specializes in multi-agent robotic systems for logistics. Their platform includes:

• Swarm coordination algorithms

• Custom hardware AMRs optimized for warehouse terrain

• An interoperable API layer that connects to WMS, ERP, and order management platforms

Zebra’s open system architecture enabled rapid integration with ColdQuanta’s quantum modules—demonstrating how quantum technologies can be injected into traditional industrial automation stacks.

Policy and Ecosystem Alignment

The collaboration also aligns with a growing policy emphasis on high-tech logistics innovation in the U.S. and globally:

• The U.S. National Quantum Initiative (NQI) encourages applied research in quantum sensing and navigation.

• DARPA’s Quantum Apertures program, although defense-oriented, lays groundwork for commercial navigation systems in GPS-denied environments.

• Colorado’s Advanced Industries Accelerator Program supports quantum and robotics startups through grants and commercialization assistance.

These programs not only funded elements of the ColdQuanta–Zebra pilot, but also enabled workforce training and access to academic partners.

Broader Industry Implications

If successful at scale, the implications of this work are significant:

• Quantum-enhanced navigation could reduce hardware cost by reducing reliance on expensive multi-modal sensor arrays.

• Quantum optimization engines could drive better orchestration in multi-AMR fleets, leading to lower warehouse operating costs and higher throughput.

• Hybrid quantum-classical algorithms could transition from simulation to edge deployment, especially as neutral atom processors mature.

Moreover, as warehouse automation continues to evolve toward lights-out fulfillment centers, robust autonomy and precise localization will become increasingly critical—and quantum offers a viable new axis of differentiation.

Looking Forward: What’s Next?

As of late 2021, ColdQuanta and Zebra Robotics were preparing to:

• Scale testing to multi-warehouse environments with >50 AMRs

• Collaborate with logistics service providers for cross-docking pilots

• Extend the quantum optimization layer to cover resource allocation, such as dynamic charging schedules and worker-robot hybrid workflows

In parallel, ColdQuanta announced plans to release an SDK for logistics-focused quantum optimization developers—encouraging the creation of third-party apps and simulations that could accelerate the adoption curve.

Conclusion: Laying the Quantum Foundation for Future Warehouses

The May 2021 pathfinder project between ColdQuanta and Zebra Robotics illustrates how even first-generation quantum technologies can deliver measurable benefits in real-world logistics settings. From enhanced localization to improved AMR coordination, the initiative shows that warehouse automation could be one of the earliest commercial beneficiaries of quantum sensing and optimization.

As both companies scale their capabilities and bring in new partners, the project stands as a model for how cross-domain collaboration—between physics researchers and robotics engineers—can lead to breakthrough innovations at the edge of quantum and logistics.

Quantum Communications Move into Supply Chain Security

Singapore’s position as one of the world’s busiest and most advanced logistics hubs has made it a natural staging ground for next-gen technology integration. In April 2021, the city-state took a decisive step into quantum-secure logistics by launching a quantum key distribution (QKD) pilot focused on the maritime supply chain.

Unlike many quantum initiatives centered on computing, this project is entirely focused on quantum-safe communication—preventing future cyber threats posed by quantum decryption capabilities and ensuring secure, tamper-proof transmission of critical logistics data.

Led by the National University of Singapore’s Centre for Quantum Technologies (CQT) and part of the government-funded National Quantum-Safe Network (NQSN), the project represents one of the earliest dedicated uses of quantum security for logistics in a real-world, operational setting.

The Threat: Logistics Vulnerabilities in the Post-Quantum Era

Logistics infrastructure is becoming increasingly digital—dependent on APIs, cloud platforms, and edge sensors. But with this shift comes risk. Today’s encryption protocols (e.g., RSA, ECC) are expected to be broken by sufficiently powerful quantum computers, potentially within the next 10–15 years.

If adversaries intercept and store encrypted logistics data today, they could decrypt it retroactively once quantum hardware matures—a phenomenon known as “harvest now, decrypt later.”

This could compromise:

• Cargo manifests and routing schedules

• Port entry permissions and customs documents

• Autonomous vehicle instructions

• Supply chain payment authentication

The QKD pilot addresses these threats proactively by ensuring forward secrecy through quantum-secure keys.

The Pilot Project: Quantum Keys at the Port

The QKD logistics trial centers on the Port of Singapore Authority (PSA)—a global transshipment hub that handles over 36 million TEUs annually. In collaboration with Singtel and SpeQtral (a Singapore-based quantum comms startup), the pilot installed QKD endpoints across:

• Terminal control centers

• Logistics command hubs

• Network interconnects between data centers and PSA’s main campus

These locations exchange sensitive real-time data about container movements, crane coordination, and routing decisions—all of which must be protected against eavesdropping or spoofing.

QKD Implementation:

The system uses entangled photon pairs transmitted through secure optical fibers to generate shared encryption keys. Any attempt to intercept the photons collapses their quantum state, revealing the presence of an intruder.

This ensures that:

• Keys are exchanged securely

• They cannot be cloned or forwarded

• Each session key is unique and ephemeral

The pilot used SpeQtral’s terrestrial QKD system, with long-term plans to expand toward satellite QKD for global inter-port security.

Real-World Results and Observations

By April 2021’s end, the pilot had run securely for over a month with zero key compromise incidents. Findings included:

• Latency below 2ms for key distribution, enabling use in real-time logistics settings.

• Key generation rate of 1 kbps, sufficient for frequent refreshes of encryption keys for voice, file transfer, and API traffic.

• High link stability, despite the fiber links passing through heavily trafficked port areas.

A joint technical report by CQT and PSA highlighted that QKD added no meaningful delay to container coordination data, debunking one of the major commercial concerns around adopting quantum security.

Strategic Importance: Singapore’s Quantum Security Blueprint

This initiative is aligned with Singapore’s broader national push to lead in quantum technologies. Key initiatives include:

• National Quantum-Safe Network (NQSN): A $12 million program to develop and trial quantum-safe communication in critical infrastructure sectors like banking, energy, and logistics.

• Quantum Engineering Programme (QEP): Funding interdisciplinary R&D across academia and industry.

• SpeQtral’s partnership with global space agencies: Exploring future deployment of satellite-based QKD for long-haul secure links.

By positioning logistics as a high-priority sector for quantum defense, Singapore is effectively hardening the foundations of its trade infrastructure before quantum threats become mainstream.

Industry Partners and Roles

The pilot’s success stems from a carefully structured collaboration:

• CQT (Centre for Quantum Technologies): Provided scientific leadership, system design, and security analysis.

• PSA International: Supplied operational use cases, data flow mappings, and integration with existing port tech stacks.

• SpeQtral: Built and installed the QKD hardware and photon transmission systems.

• Singtel: Managed secure optical fiber infrastructure between port nodes and oversaw telecom compliance.

Together, these entities showed how quantum research can translate into production-grade cybersecurity solutions in logistics.

Use Case Expansion: More Than Just the Port

Beyond port applications, the QKD infrastructure opens doors for secure communications in:

• Cold chain logistics hubs (e.g., biomedical supply routes)

• Airport cargo terminals

• Regional bonded warehouses connected via dark fiber

• Blockchain-enabled logistics platforms, where smart contracts can be quantum-hardened

Additionally, PSA and SpeQtral are now exploring vehicle-to-infrastructure (V2I) QKD for autonomous trucks and cranes within port precincts—an area previously considered unfeasible due to latency sensitivity.

Future Outlook: Scaling and Satellites

While the April 2021 trial proved QKD viable at the port level, the next challenge is inter-port communication security. The following steps are on Singapore’s roadmap:

1. Integrate QKD with customs clearance systems for secure data exchange between port and government.

2. Link to other Asian and European ports via optical fiber and future satellite QKD channels.

3. Collaborate on standardization efforts under the ETSI and ISO quantum-safe working groups.

By embedding QKD early, Singapore is not just defending today’s supply chain but future-proofing global trade links against the coming quantum decryption wave.

Conclusion: Logistics Security in the Quantum Age

Singapore’s April 2021 QKD pilot demonstrates a key truth: defending the future of logistics is not just about faster ships or smarter warehouses—it’s about securing the invisible threads that bind these systems together. In a hyperconnected, post-quantum world, unbreakable communication may be as critical as physical cargo flows.

As one of the first real-world deployments of QKD in logistics, this project sets a precedent. Other major trade hubs—Rotterdam, Dubai, Shanghai—will be watching closely, as quantum-secure logistics moves from theory into critical infrastructure reality.

From Last Mile to Last Meter: Quantum Routing Inside Warehouses

As e-commerce volume surged in 2020–2021, DHL Supply Chain—responsible for some of the world's busiest fulfillment centers—faced an acute challenge: ensuring fast, conflict-free movement of thousands of orders per hour across complex warehouse layouts.

In April 2021, DHL partnered with D-Wave Systems, a Canadian leader in quantum annealing, to prototype a quantum-enhanced routing system. The goal: accelerate and streamline AGV and picker routing across constrained warehouse zones, where frequent path recalculations and traffic bottlenecks slow fulfillment rates.

This approach shifts the spotlight from macro-logistics (e.g., port scheduling, long-haul routing) to micro-logistics optimization, where the complexity of navigation within facilities often goes underappreciated.

Quantum Annealing Meets Intra-Warehouse Traffic

The DHL-D-Wave pilot focuses on using quantum annealing, a quantum computing technique particularly suited for solving combinatorial optimization problems such as the shortest path problem, traveling salesman problem (TSP), and graph coloring.

In warehouse operations, these map directly to:

• Dynamic route assignment for AGVs

• Real-time collision avoidance

• Workforce path optimization under varying workloads and zones

Key Parameters Modeled:

• AGV battery life constraints

• Time windows for order fulfillment

• Avoidance of high-traffic choke points

• Aisle width and obstacle clearance

• Variable zone priorities based on order urgency

These variables were encoded into QUBO (Quadratic Unconstrained Binary Optimization) models and run on D-Wave’s Advantage quantum processor via the Leap cloud service.

Results from the Pilot Phase

The pilot testbed, conducted in one of DHL’s major European fulfillment hubs in Germany, targeted a specific high-density storage zone with frequent AGV congestion. Over a two-week period, DHL and D-Wave collected the following results:

• 13–15% reduction in total AGV transit time compared to classical routing heuristics.

• 22% decrease in AGV idle time, particularly during peak load conditions.

• Notably, the quantum annealing approach consistently found more efficient paths in scenarios with multiple conflicting agents, something classical solvers struggled to resolve in real time.

While these gains may appear incremental, they scale significantly in high-volume environments. Even a 5% gain in AGV throughput can unlock millions in annual fulfillment capacity.

Why Quantum Annealing Was Chosen

DHL's decision to work with D-Wave—rather than gate-based quantum systems—was strategic. Annealing-based machines like D-Wave’s are:

• Commercially available now, with scalable access via cloud.

• Tailored for combinatorial problems, which dominate warehouse routing.

• Faster for near-term applications, where quantum advantage can be found in hybrid systems.

According to DHL Supply Chain’s Head of Innovation Europe, Marco Stoll:

“Quantum annealing gives us an early opportunity to solve logistics problems that classical systems struggle with during dynamic rerouting. This isn’t future-gazing—it’s a near-term accelerator for warehouse efficiency.”

Building the Quantum Warehouse Stack

The system architecture built during the pilot combined:

1. Data Capture Layer: Live telemetry from AGVs, warehouse layout maps, real-time SKU pick rates, and congestion indicators.

2. QUBO Model Generator: Developed by D-Wave engineers and DHL analysts to encode routing challenges into quantum-annealable structures.

3. Hybrid Solver Stack: Combined D-Wave’s quantum annealer with classical pre- and post-processing layers to refine solution quality.

4. Visualization Interface: Integrated with DHL’s existing warehouse management system (WMS) to allow human operators to override or validate quantum-suggested paths.

This architecture reflects a hybrid quantum-classical model, expected to dominate near-term industrial quantum applications.

Expanding the Use Case: From AGVs to Humans

Beyond AGV routing, DHL is also exploring human picker path optimization. In sprawling fulfillment centers, pickers walk several kilometers per shift. Minor improvements in route planning can yield significant ergonomic and throughput gains.

Quantum-optimized picker routing could:

• Reduce walking distances by clustering orders more effectively.

• Adapt routes dynamically as priorities shift during a shift.

• Integrate with robotics to synchronize human-machine workflows.

This focus on “last-meter logistics” differentiates DHL’s quantum strategy from others pursuing broader network-level optimizations.

Collaboration and Ecosystem Benefits

The pilot benefited from strong collaboration between:

• D-Wave Systems: Providing annealing access and QUBO engineering support.

• DHL Supply Chain's Applied Analytics Unit: Translating operational challenges into optimization variables.

• RWTH Aachen University: Providing academic oversight and simulation validation.

The use of D-Wave’s Leap hybrid platform also made it possible to test dozens of daily scenarios at scale without investing in physical quantum hardware—demonstrating a quantum-as-a-service (QaaS) model with fast deployment potential.

Next Steps: Toward Scaled Deployment

Following the April 2021 success, DHL Supply Chain laid out a phased roadmap:

• Q3 2021: Expand pilots to North American facilities with higher AGV density.

• 2022: Integrate quantum routing module into select WMS and AGV control systems.

• 2023 and beyond: Extend use cases to multi-warehouse networks and cross-docking logistics.

DHL also expressed interest in quantum-enhanced facility layout planning and inventory zoning decisions, both of which involve hard optimization problems well-suited for annealing systems.

The Broader Signal: Quantum for Operational Efficiency

This project signals a shift from conceptual quantum logistics to operational quantum logistics. Rather than focus on abstract future potential, DHL and D-Wave have shown that real performance improvements are already possible with today’s quantum resources—especially when applied to well-scoped, high-impact micro-problems.

As fulfillment expectations continue to rise and labor availability fluctuates, quantum-powered routing may soon become a standard part of the logistics tech stack—especially in facilities with high AGV and robotic throughput.

Conclusion: Optimizing the Unseen

While shipping routes and delivery drones capture headlines, most logistics friction still happens inside the warehouse. With their April 2021 pilot, DHL and D-Wave demonstrated how quantum annealing can remove hidden inefficiencies and improve operational flow where it matters most—inside the fulfillment center.

As quantum hardware matures and hybrid solvers become easier to deploy, the “quantum warehouse” could become a global benchmark for how logistics firms unlock value from next-generation technologies.

A Strategic Partnership for Quantum Supply Chain Resilience

The COVID-19 pandemic exposed systemic weaknesses in global supply chains—from port bottlenecks to SKU-level inventory shocks. In response, MIT CTL and Maersk joined forces in April 2021 to form a multi-stakeholder consortium focused on leveraging quantum computing to model, simulate, and optimize supply chain resilience at global scale.

The partnership includes:

• MIT CTL: Renowned for its supply chain innovation and systems modeling.

• Maersk: One of the world’s largest integrated container logistics firms.

• QCWare and Zapata Computing: Quantum software firms providing cloud-based quantum computing access.

• Amazon Braket and IBM Q: As infrastructure partners for quantum simulations.

The goal: To co-develop quantum-classical hybrid algorithms that can better navigate uncertainties in logistics—such as demand fluctuations, supplier disruptions, and global transport volatility.

Quantum Modeling of Supply Chain Risks

Traditional supply chain models often rely on linear programming and Monte Carlo simulations. While powerful, these methods face limits when dealing with:

• Nonlinear interdependencies

• Multi-echelon disruptions

• Exponential combinations of routing and inventory decisions

Quantum computing, particularly variational quantum algorithms (VQAs) and quantum approximate optimization algorithms (QAOA), can model such problems more efficiently by exploring multiple states simultaneously.

Research Areas:

1. Inventory Risk Optimization: Using QAOA to identify optimal safety stock levels across multi-tiered networks under volatile demand.

2. Supplier Resilience Modeling: Applying quantum-inspired probabilistic simulations to model upstream cascading effects from supplier shutdowns.

3. Global Routing Alternatives: Evaluating port-to-port rerouting options using quantum-enhanced network flow analysis under time and cost constraints.

The Consortium’s Structure and Objectives

The Quantum Logistics Research Consortium is structured around quarterly sprints and annual goals. In 2021, the consortium committed to:

• Running quantum experiments using simulated supply chain disruptions (e.g., Suez Canal blockage scenarios).

• Developing a benchmark suite of logistics optimization problems for quantum solvers.

• Publishing white papers and open-access datasets to accelerate industry-wide learning.

An advisory board with representatives from the U.S. Department of Transportation, Port of Los Angeles, and European Logistics Association provides strategic oversight and ensures global applicability.

Early Pilot Results

In April 2021, the consortium completed its first round of benchmark testing using synthetic supply chain data modeled on Maersk’s Europe-to-Asia shipping routes. Results included:

• A 14% improvement in inventory risk balancing using hybrid quantum-classical algorithms over classical-only simulations.

• Identification of alternate port routing strategies in real-time that reduced projected lead times by up to 9%.

• Quantum-enhanced simulations required 40% fewer runs to converge on risk-optimal configurations compared to traditional stochastic models.

While still early-stage, these findings suggest that quantum methods can reduce compute cost and improve response time in complex supply chain simulations.

Why Maersk and MIT See Quantum as a Long-Term Bet

For Maersk, investing in quantum research is a long-term strategy to future-proof its global operations. According to Maersk CTO Ken Lundeberg:

"Resilience is now a boardroom-level priority. The complexity of our network demands tools that go beyond traditional linear models. Quantum optimization holds the promise of scalable, adaptive supply chain intelligence."

MIT CTL Director Dr. Yossi Sheffi emphasized the academic value:

"We’re not just proving the power of quantum computing—we’re redesigning how we model global logistics networks under stress. This is as much a rethink of supply chain science as it is a technology pilot."

Global Interest and Alignment with Policy

The initiative aligns with growing interest in resilient infrastructure from governments and trade bodies. Key connections include:

• U.S. National Quantum Initiative: Funding foundational quantum research with logistics applications.

• World Economic Forum (WEF): Promoting “quantum advantage in resilience” as part of its supply chain reboot agenda.

• UNCTAD: Incorporating quantum-readiness into port digitalization strategies for developing economies.

This positioning ensures that outputs from the MIT-Maersk consortium are not siloed but feed into global resilience-building programs.

Quantum Workforce Development in Logistics

Another core pillar of the project is education. As part of the April launch, MIT CTL announced the creation of a new executive certificate program on Quantum Computing for Supply Chain Leaders, aimed at:

• Logistics professionals

• Port authorities

• Freight forwarders

• Public infrastructure planners

The course includes modules on:

• Quantum basics for operations research

• Case studies on quantum-enhanced demand planning

• Hands-on labs using IBM Q and Amazon Braket environments

This education component helps address one of the biggest barriers to quantum adoption: the skills gap between quantum researchers and supply chain professionals.

What Comes Next: 2022 and Beyond

The consortium has laid out a roadmap extending into 2023:

• 2022 Q1–Q2: Real-world simulations using anonymized Maersk shipment data.

• 2022 Q3: Joint publication on quantum resilience modeling.

• 2023: Test pilots with additional partners including FedEx and the Port of Hamburg.

MIT and Maersk are also in discussions with logistics insurers and risk assessment firms to evaluate how quantum-enhanced risk modeling might transform insurance premiums and policies.

Conclusion: Building Quantum Resilience, Not Just Speed

While much of the early excitement around quantum computing focused on speed, this initiative reframes the conversation around resilience. The ability to simulate countless disruption scenarios and optimize recovery paths could give logistics leaders a powerful edge in navigating the next global crisis—be it pandemic, climate event, or geopolitical disruption.

By combining MIT’s research prowess with Maersk’s operational depth and quantum software capabilities from startups like QCWare and Zapata, this April 2021 milestone sets the stage for a new era of intelligent, disruption-proof supply chains.

Maersk's Inland Logistics Challenge

As the world’s largest container shipping company, Maersk operates not only at sea but also across vast inland haulage networks. These cover tens of thousands of trucking routes connecting ports to final destinations. The complexity of optimizing this network in real-time has become a critical challenge as supply chain disruptions, carbon regulations, and customer demands grow more volatile.

In response, Maersk launched a pilot with QC Ware to explore whether near-term quantum machine learning (QML) tools could enhance dynamic routing and fleet deployment decisions beyond what current algorithms offer.

Project Goals: Sustainable Optimization at Scale

The core objective of the April 2021 pilot was to:

• Reduce carbon emissions by minimizing truck mileage and idling.

• Improve cost efficiency via better asset allocation and load balancing.

• Enhance responsiveness to port congestion and weather-related disruptions.

The pilot focused on logistics hubs in Germany, Belgium, and the Netherlands, targeting major intermodal corridors where port throughput is high and traffic conditions are unpredictable.

QC Ware’s Role: Quantum-Ready Algorithms for Trucking Logistics

QC Ware, a Palo Alto-based quantum software firm, brought to the table its Forge platform, which provides access to hybrid quantum-classical algorithms that can run on both simulators and real quantum hardware.

Key technical strategies included:

• QML models trained on port data, route histories, and weather feeds.

• Quantum-enhanced k-means clustering to group similar transport tasks.

• Variational Quantum Circuits (VQC) used to explore optimized route groupings under constraints.

QC Ware engineers worked with Maersk’s data science and transport planning teams to translate route planning problems into quantum-classical representations suitable for hybrid computing workflows.

Simulation Environments and Constraints

The Maersk-QC Ware team ran simulations on synthetic and anonymized datasets mimicking:

• Daily truck dispatch volumes from Rotterdam, Antwerp, and Bremerhaven.

• Real-time traffic variability and incident delays.

• Emission thresholds and delivery time window constraints.

A key challenge involved encoding vehicle routing problems (VRPs) into a format solvable by Noisy Intermediate-Scale Quantum (NISQ) devices, given current hardware limitations.

Initial Findings from the April 2021 Pilot

While full deployment on live fleets was not attempted during the pilot, initial simulation results showed:

• 5–8% reduction in total travel distance compared to Maersk’s classical baseline.

• Improved fleet utilization efficiency in multi-stop trip scheduling.

• Early signs that quantum-enhanced clustering led to more robust grouping of delivery requests under uncertainty.

The pilot also highlighted the importance of hybrid architecture, where classical preprocessing and post-processing are paired with quantum optimization cores.

Environmental and Strategic Implications

For Maersk, this pilot aligned with broader environmental and digital transformation goals:

• The company has pledged to become net-zero by 2040 and seeks technologies that cut emissions without compromising service levels.

• Maersk’s growing inland business demands fine-grained optimization tools as more operations move from ocean-centric to door-to-door logistics.

QC Ware’s quantum-enhanced models promise incremental improvements today and significant gains as quantum hardware matures.

Industry Context: Quantum in Freight and Route Optimization

This pilot adds to growing interest in quantum applications in transport logistics:

• DHL and Terra Quantum (2022) tested quantum-inspired rerouting.

• DB Schenker explored quantum-secure communications in trucking networks.

• Kuehne+Nagel began investigating quantum-enhanced demand forecasting.

Maersk’s early involvement positions it to lead in the long-term shift toward quantum-optimized supply chains, especially as quantum computers become more accessible via cloud platforms.

Talent and Infrastructure Considerations

Maersk’s data science team acknowledged a steep learning curve in working with quantum tools. To address this:

• QC Ware provided training and co-development sessions.

• The pilot included developing explainability layers to help planners interpret QML suggestions.

• QC Ware’s Forge was run in the cloud, minimizing infrastructure friction for Maersk.

Next Steps and Roadmap

Following the April pilot, Maersk indicated interest in:

• Extending the quantum models to rail and barge segments in Europe.

• Testing quantum optimization for container repositioning problems.

• Exploring integration into Maersk’s TradeLens blockchain-based visibility platform.

A follow-up proof-of-concept phase is scheduled for 2022, contingent on improvements in quantum simulation speed and hardware access.

Conclusion: Quantum Trucking Optimization Enters the Real World

This pilot marks a quiet but important milestone: quantum machine learning is beginning to address real-world operational logistics challenges at the enterprise level.

For Maersk, it represents an initial step toward a future where quantum-enabled decision engines might operate in tandem with classical analytics to drive precision, sustainability, and competitiveness in global logistics.

Urban Logistics Meets Quantum Computing

By early 2021, urban logistics systems were under significant pressure. Rising e-commerce activity, strict emissions regulations, and growing demand for same-day delivery had outpaced the capabilities of traditional static routing systems. Municipalities and logistics operators alike were seeking adaptive fleet management technologies that could respond to real-time conditions and reduce inefficiencies.

Volkswagen had already been exploring quantum computing applications since 2017, when it first partnered with D-Wave to experiment with traffic flow optimization during the Web Summit in Lisbon. In March 2021, the companies took a more advanced step: deploying a dynamic fleet routing system powered by quantum annealing to manage real-time vehicle dispatch for an urban logistics operator.

Project Scope and Objectives

The pilot was designed with four goals:

1. Minimize empty miles traveled by light-duty delivery vans and urban passenger shuttles.

2. Respond in real time to traffic disruptions, demand surges, and environmental factors.

3. Integrate zero-emission vehicle constraints, ensuring EVs are routed according to battery levels and charging station availability.

4. Demonstrate commercial viability of quantum-based dispatch models for last-mile logistics.

The deployment focused on a mid-sized European city with a mixed fleet of over 100 vehicles, including e-vans for parcel delivery and autonomous minibuses for public transport and micro-mobility use.

Why Quantum for Urban Routing?

Urban dispatching is a variant of the Vehicle Routing Problem (VRP)—a classic NP-hard problem where complexity increases exponentially with the number of delivery points, vehicles, constraints, and real-time inputs.

While classical heuristics like Clarke-Wright savings or Tabu search can deliver reasonable routes, they struggle with:

• Dynamic reallocation when demand or conditions change during operations.

• High-dimensional constraints such as emissions caps, road closures, or EV range anxiety.

• Multiple overlapping objectives (cost, time, CO₂, service quality).

Quantum annealers like D-Wave’s 2000Q system can map VRPs into QUBO (Quadratic Unconstrained Binary Optimization) formulations. These are particularly well-suited to representing multiple constraints and exploring diverse potential solutions quickly.

The Technical Model

The routing system combined three primary layers:

1. Data Ingestion & Preprocessing

A real-time stream of GPS data, traffic conditions, package demand, and EV battery levels was ingested into a classical preprocessing pipeline. This produced time-sensitive snapshots of fleet status and delivery priorities.

2. Quantum Optimization Layer

Key dispatch decisions (e.g., which vehicle to send, what route to follow, when to reallocate) were encoded as QUBO problems and sent to the D-Wave quantum processor.

Constraints included:

• Delivery time windows

• EV battery capacity

• Road congestion data

• Vehicle capacity limits

• Environmental zones with restricted access

Using hybrid quantum-classical solvers, the system evaluated thousands of routing combinations in parallel and identified sets of near-optimal reassignments.

3. Execution & Feedback Layer

Quantum recommendations were translated into route updates and vehicle assignments via APIs to Volkswagen’s fleet management platform. Results were fed back into the system to continuously update the state space.

Real-World Testing: Results from the Field

The March 2021 pilot ran for four weeks and yielded promising performance indicators:

A. Route Efficiency

• Average route distance dropped by 5.8%, attributed to better matching of vehicles to tasks based on real-time demand.

• For EVs, optimized routing led to 13% fewer charging events, minimizing unnecessary detours.

B. Empty Mileage Reduction

• The percentage of fleet kilometers driven without cargo or passengers dropped from 21.3% to 17.9%, a relative reduction of 16%.

C. Response to Disruptions

• During live testing, the system dynamically rerouted vehicles around construction zones and an unexpected protest march, achieving rerouting latencies under 12 seconds.

D. Emissions Improvement

• CO₂-equivalent emissions were cut by 7.5%, largely due to more efficient vehicle utilization and fewer idling scenarios in traffic bottlenecks.

E. Service Quality

• On-time delivery rates improved from 87.4% to 93.1%, especially during peak hours.

These results, while modest in absolute terms, validated the potential for quantum computing to bring tangible improvements to city-scale logistics networks.

Industry and Ecosystem Context

Volkswagen’s Quantum Strategy

Volkswagen’s Data:Lab in Munich had been actively researching quantum use cases including battery chemistry, production scheduling, and traffic optimization. The March 2021 pilot marked its first logistics-oriented test at operational scale.

According to Florian Neukart, Volkswagen’s Director of Advanced Technology Planning at the time, “Quantum computing allows us to optimize what was previously too complex or computationally expensive to handle in real time. Urban dispatch is a prime candidate.”

D-Wave’s Role

D-Wave supplied not only the hardware but also a hybrid solver stack—blending classical pre-solvers with quantum processing and post-analysis tools. Its Leap cloud platform allowed rapid prototyping and tuning of QUBO formulations.

D-Wave has previously collaborated with logistics firms like DHL and Toyota, but this project marked a shift toward operational pilots rather than academic showcases.

Municipal Support

The local transport authority provided support in terms of access to traffic management systems and integration with low-emission zone regulations, aligning the pilot with broader urban sustainability objectives.

Broader Implications for Last-Mile Logistics

As cities grow more complex and climate pressures increase, logistics networks must become more intelligent and adaptive. This pilot offered several key takeaways for industry:

1. Quantum Adds Value in Dynamic Scenarios

While classical systems perform well in static planning, quantum systems excel when rapid re-optimization is needed, such as in the face of delays, cancellations, or sudden demand surges.

2. Hybrid Is the Future

Rather than fully replacing traditional routing engines, quantum computing complements them—especially in narrow but computationally intense subproblems, such as zone-based vehicle reallocation.

3. Edge Integration Is Viable

The project demonstrated that quantum-assisted recommendations could be ingested by edge dispatch systems and acted on within seconds—a key requirement for operational adoption.

Next Steps and Roadmap

Following the March pilot, Volkswagen and D-Wave outlined a roadmap for expansion:

• Scale Up: Expand the system to more cities and a larger vehicle pool (500+).

• Multimodal Optimization: Integrate with bike couriers, drones, and autonomous sidewalk bots.

• EV-Specific Routing: Incorporate battery degradation models and dynamic charging prices.

• Sustainability Goals: Partner with cities to align with urban decarbonization plans.

A broader initiative to support quantum-native mobility platforms is now in motion within Volkswagen’s innovation units, with further field pilots planned in Germany and North America through 2022.

Conclusion: Routing Toward a Quantum Future

The March 2021 collaboration between Volkswagen and D-Wave marked a turning point in practical quantum logistics. By proving that quantum-enhanced routing can operate in a live urban environment—reducing inefficiencies, emissions, and response time—the pilot laid the foundation for a new class of real-time logistics optimization tools.

As more cities and companies embrace carbon neutrality and smart transport goals, quantum dispatch models may soon become a competitive differentiator for fleet operators looking to deliver faster, greener, and smarter.

The Post-Pandemic Supply Chain Challenge

Global supply chains in early 2021 remained under pressure. From raw material delays to sudden demand spikes, the lingering aftershocks of COVID-19 had exposed the fragility of just-in-time inventory models. Retailers, especially in fast-moving consumer goods (FMCG) and apparel, found themselves caught between demand uncertainty and upstream supply volatility.

Accenture's Supply Chain & Operations division began exploring advanced forecasting and synchronization tools. One promising path: quantum computing.

Enter IonQ—one of the leading developers of gate-based quantum computers based on trapped-ion technology. In early 2021, IonQ was among the first to offer commercial access to a quantum machine via the cloud. Their hardware's high fidelity and all-to-all qubit connectivity made it a strong candidate for combinatorial optimization and probabilistic modeling.

Project Overview: Supply Chain Synchronization with Quantum Support

The collaboration between Accenture and IonQ focused on a core problem in global supply chain orchestration: temporal misalignment across tiers.

From Tier 3 raw material suppliers to Tier 1 contract manufacturers, and finally to last-mile distributors, supply chains often suffer from:

• Forecast divergence between sales and procurement teams

• Lead time variability in cross-border shipments

• Inventory misallocation across distribution centers

• Production overhangs due to poor upstream-downstream signaling

The joint project sought to prototype a quantum-supported synchronization model to:

• Minimize latency in multi-echelon planning

• Reduce stockouts and overstock simultaneously

• Adapt dynamically to external disruptions (port delays, supplier issues)

• Improve service level agreement (SLA) adherence across the chain

Quantum Modeling Approach

Architecture

The quantum component was structured to work alongside classical planning tools (ERP/MRP systems) rather than replace them. The architecture included:

1. Classical Preprocessing Layer: Aggregates SKU-level forecasts, historical demand patterns, supplier performance data, and transit lead times.

2. Quantum Optimization Layer: Encodes a multi-tier synchronization problem as a Stochastic Supply Alignment Problem (SSAP), modeled into QUBO or Hamiltonian form for quantum execution.

3. Post-Processing and Integration: Translates quantum results (synchronized replenishment cycles, buffer thresholds) into actionable scheduling updates for ERP systems.

Quantum Hardware Details

IonQ’s trapped-ion device offered key advantages:

• High two-qubit gate fidelity (~99%)

• Full connectivity between qubits, minimizing overhead for dense optimization models

• Long coherence times suitable for deep circuit execution (required for modeling temporal dependencies)

Pilot Use Case: Apparel Retail Chain with Global Suppliers

The first pilot client was a U.S.-based fast fashion retailer with global sourcing operations. Specific supply tiers included:

• Raw materials from India and Turkey

• Manufacturing partners in Vietnam

• Regional warehouses in Mexico and the southeastern U.S.

• Brick-and-mortar and online retail outlets

The synchronization model was applied to seasonal apparel collections, where accurate alignment of materials, production slots, and launch windows is crucial.

Key Results and Impact

From March to early April 2021, Accenture and IonQ ran the synchronization model in a simulated production environment, producing the following results:

1. Inventory Balance Improvement

The model reduced overstock in DCs by 14% while improving shelf availability by 9% for high-demand SKUs, compared to the retailer’s classical planning baseline.

2. Lead Time Risk Mitigation

Simulations showed a 22% decrease in missed production starts due to improved raw material arrival forecasts and better slot coordination with suppliers.

3. SLA Adherence

Fulfillment SLA breaches dropped by 16%, especially for priority channels such as online orders and flagship stores.

4. Forecast Variance Management

By encoding probabilistic demand profiles in the quantum layer, the model helped harmonize planning across departments—reducing demand signal divergence between sales and procurement by over 25%.

These were not quantum supremacy-style breakthroughs, but meaningful, measurable gains in a chaotic supply environment—especially notable for a technology still in early stages of commercial maturity.

Second Pilot: Consumer Packaged Goods (CPG) Client

A second pilot involved a multinational CPG brand managing high-turnover household products. Here, the challenge centered on supplier bottlenecks and real-time demand shocks in Q1 2021, as panic-buying and weather disruptions coincided.

Quantum-enhanced synchronization helped identify shared supplier constraints across product families and suggested batch rescheduling, which lowered stockouts by 11% during peak volatility weeks.

Strategic Implications for Global Supply Chains

The Accenture–IonQ collaboration represents more than a one-off pilot—it suggests a viable long-term model for integrating quantum solutions into enterprise supply systems. Key takeaways:

A. Complementarity, Not Replacement

Quantum algorithms worked alongside classical systems like SAP and Oracle SCM, acting as scenario simulators and coordination enhancers rather than replacing existing tools.

B. Multi-Tier Coordination Edge

Quantum’s ability to evaluate thousands of interaction permutations simultaneously offered a decisive edge in aligning upstream and downstream tiers—something classical linear programming often handles sub-optimally under uncertainty.

C. Early Business Value Without Full-Scale Quantum

The pilots leveraged hybrid quantum-classical workflows, requiring only tens of qubits—proving that valuable insights are possible even with today’s NISQ (Noisy Intermediate-Scale Quantum) hardware.

Roadmap: From Prototype to Platform

Following the March 2021 release, Accenture announced the formation of a Quantum Logistics Co-Innovation Lab in collaboration with IonQ and selected retail clients.

Planned initiatives include:

• Creating standardized quantum modules for supply planning, forecasting, and demand-supply matching

• Integrating with Microsoft Azure and AWS cloud quantum services

• Training supply chain analysts in quantum model interpretation and integration

The goal: move from proofs-of-concept to repeatable, modular solutions deployable across diverse supply networks.

Limitations and Next Steps

The report noted certain limitations:

• Scalability: Current quantum hardware supports only mid-size instances of synchronization problems; large retail chains will need model decomposition.

• Interpretability: Quantum-derived solutions require explainability layers for planner adoption.

• Integration Costs: Embedding quantum layers into existing IT stacks requires middleware and interface development.

Still, Accenture sees growing commercial demand from retailers seeking robustness and agility in post-COVID supply networks.

Conclusion: Quantum Synchronization as a Logistics Differentiator

March 2021 marked a turning point in quantum supply chain applications. Accenture and IonQ’s joint pilots delivered a compelling case for early-stage quantum value—especially in synchronizing multi-tier retail supply chains, one of the most complex challenges in logistics today.

As quantum hardware and software tools evolve, their integration into supply ecosystems may transition from experimental to essential. Synchronization, long an elusive goal in global logistics, may find its best ally not in more data—but in a better way to compute it.

The Cold Chain Problem: A Quantum Opportunity

Cold chain logistics—where temperature-sensitive goods such as vaccines, dairy, or seafood must be transported under tightly controlled thermal conditions—poses one of the most complex routing challenges in logistics. In March 2021, quantum computing firm D-Wave teamed up with VersaCold, Canada’s largest temperature-controlled supply chain company, to pilot a solution that could redefine how such logistics networks are optimized.

D-Wave’s quantum annealers are particularly well-suited for combinatorial optimization problems, such as route planning, packing, and scheduling—areas where cold chain operators often face nonlinear trade-offs between time, energy use, and product integrity.

This pilot marked one of the first real-world applications of hybrid quantum-classical optimization in the cold chain domain, paving the way for more efficient and resilient temperature-sensitive deliveries.

Why Cold Chain Logistics Needs Quantum Optimization

Unlike standard parcel delivery, cold chain logistics has several added constraints:

• Time windows: Perishable goods must arrive within strict timeframes.

• Temperature sensitivity: Vehicles must avoid routes that cause delays or temperature breaches.

• Multi-depot coordination: Warehouses across provinces often serve overlapping customer regions.

• Dynamic conditions: Weather, traffic, and vehicle health can alter real-time decisions.

Classical solvers struggle to account for all these factors in large-scale, real-time route optimization. Quantum annealing offers a novel approach—one that evaluates thousands of near-optimal solutions simultaneously, capturing trade-offs that traditional heuristics might miss.

The Pilot Setup: Routes, Goals, and Quantum Architecture

Scope and Dataset

The pilot focused on VersaCold’s cold chain network connecting Calgary, Edmonton, Vancouver, and surrounding regions. It involved:

• 5 cold storage hubs

• 26 daily delivery vehicles

• 127 active customer locations

• 3 product categories with different handling times and temperature thresholds

Objectives

The project aimed to:

• Minimize total delivery time while meeting strict time windows

• Reduce mileage and fuel consumption across vehicle fleets

• Lower the rate of rejected shipments due to thermal excursions

• Model resilience to real-time traffic and weather changes

Technical Architecture

The optimization engine used D-Wave’s Leap hybrid solver service, combining:

• Classical pre-processing to generate feasible delivery windows and constraints

• Quantum annealing for solving a time-constrained Vehicle Routing Problem (VRP) expressed in QUBO (Quadratic Unconstrained Binary Optimization) form

• Post-processing to map quantum outputs into dispatch-ready delivery schedules

Key Innovations in Quantum Routing

The March 2021 pilot demonstrated how quantum optimization brings unique strengths to cold chain routing:

1. Time-Window Aware VRP

By encoding time-window penalties into the QUBO formulation, the solver could prioritize early deliveries to the most time-sensitive locations—especially for high-risk goods like medical supplies.

2. Fuel-Efficient Route Selection

The hybrid solver integrated fuel usage estimations, enabling the routing algorithm to avoid high-traffic corridors and steep elevation changes that spike fuel consumption and increase container temperature variability.

3. Risk Scoring Overlay

Quantum-generated routes were combined with historical delivery failure data (e.g., delays, temperature alarms) to produce risk-adjusted schedules—a breakthrough for managing operational uncertainty in a real-world logistics network.

Results from the March 2021 Pilot

The three-week pilot concluded with the following performance highlights:

• 12% reduction in total route distance compared to VersaCold’s classical dispatch software.

• 9% fewer missed time windows, especially in Calgary–Edmonton corridors.

• 15% decrease in temperature threshold violations during deliveries, attributed to shorter routes and lower congestion exposure.

• Delivery simulations under high-traffic scenarios showed the hybrid solver produced alternate routes within 90 seconds, outperforming the company’s existing re-routing module.

D-Wave noted that while their annealers do not yet provide exponential speedup, the diversity and resilience of solutions were key differentiators over single-path classical heuristics.

From Pilot to Practice: Roadmap and Scaling Strategy

Following the pilot’s success, D-Wave and VersaCold outlined a joint roadmap:

• Q2 2021: Integration of live vehicle telemetry for real-time schedule adjustments.

• Q3 2021: Expansion of the routing engine to cover Eastern Canada, including Ontario and Quebec.

• Q1 2022: Embedding quantum route recommendations into VersaCold’s dispatch dashboard, with explainability features for driver and planner buy-in.

Additionally, both parties plan to open source key components of the QUBO model for community adaptation across different cold chain verticals.

A Blueprint for Other Cold Chain Networks

The success of this pilot offers a transferable model for other cold chain operators facing similar challenges:

• Pharmaceuticals (e.g., COVID-19 vaccine distribution)

• Frozen and fresh food retailers

• Floral and biological specimen transport

With increased attention to ESG (Environmental, Social, Governance) metrics, the fuel and spoilage reductions enabled by quantum routing also align with sustainability goals across the sector.

Broader Quantum Logistics Context

This pilot fits into a growing trend in 2021: applying hybrid quantum methods to real-world logistics. Other examples from the same period include:

• Volkswagen’s quantum traffic routing tests in Beijing and Barcelona

• DHL’s simulation of warehouse picker routing using tensor networks

• Airbus exploring quantum routing for spare parts in aerospace supply chains

By proving that quantum solvers can handle real delivery constraints, the D-Wave–VersaCold partnership shifted the narrative from academic feasibility to operational value.

Challenges and Future Improvements

Despite encouraging results, the project surfaced limitations:

• Solver tuning complexity: Penalty weights in the QUBO needed precise calibration to avoid infeasible or suboptimal solutions.

• Hardware limits: Scaling to larger geographic areas will require more advanced quantum processors or decomposition strategies.

• Operational integration: Human dispatchers need trust in the quantum engine’s suggestions, which requires robust explainability tools.

Both companies agreed to pursue AI–quantum co-optimization, blending machine learning demand forecasts with quantum routing in future phases.

Conclusion: A Cold Chain Quantum Milestone

The March 2021 collaboration between D-Wave and VersaCold marks a pivotal moment in quantum logistics. By demonstrating tangible value in cold chain routing—one of the most unforgiving domains in supply chain planning—the pilot has inspired renewed interest in bringing quantum optimization into operational settings.

As quantum hardware matures and hybrid frameworks improve, cold chain networks around the world may soon be cooled not just by refrigeration units, but by quantum algorithms ensuring they’re in the right place, at the right time, under the right conditions.

A First-of-Its-Kind Quantum Tool for Warehouse Design

Warehouse operations are often bogged down by complex workflows involving conveyors, robots, sorting arms, and real-time inventory flows. Honeywell, a legacy leader in industrial automation, joined forces with Microsoft’s Azure Quantum team in March 2021 to tackle these challenges by launching a hybrid quantum-classical simulator explicitly aimed at warehouse design and operations.

The project marks one of the earliest known attempts to integrate quantum computing with warehouse simulation platforms—an area previously dominated by classical tools like FlexSim, AnyLogic, and Siemens Plant Simulation.

The platform is capable of:

• Modeling multi-robot coordination in constrained warehouse spaces

• Solving inventory bin-slotting problems under high-dimensional constraints

• Testing throughput optimization strategies using quantum-inspired solvers

Why Quantum Simulation Matters for Warehousing

Modern warehouses, especially e-commerce fulfillment centers, resemble complex micro-cities—with hundreds of autonomous guided vehicles (AGVs), conveyor loops, dynamic SKU sorting, and real-time data inputs. Classical simulations often hit computational limits when:

• Optimizing multiple robotic paths to avoid collision

• Balancing throughput vs. energy consumption

• Scheduling restocking routes during unpredictable demand

Quantum computing holds the potential to tackle these NP-hard problems more effectively by exploring vast solution spaces in parallel. Honeywell’s trapped-ion quantum processors, among the most stable in the industry, are central to this effort.

Platform Capabilities and Design

The platform is built on three integrated layers:

1. Digital Twin Generator
   A cloud-based module that converts warehouse CAD layouts and robotic configurations into simulated environments, complete with demand profiles, failure probabilities, and power consumption metrics.

2. Quantum Optimization Engine (QOE)
   Using QUBO and CVRP models, the QOE assigns:

• Task schedules to robots

• Optimal paths for AGVs to minimize energy and time

• Slotting strategies that account for product velocity and picking frequency

1. Visualization and Analytics Dashboard
   Provides operations managers with KPI heatmaps, bottleneck forecasts, and simulation playback features to analyze throughput under various configurations.

The QOE leverages Microsoft’s Azure Quantum APIs to access both quantum hardware (Honeywell’s H-Series processors) and quantum-inspired solvers from the Microsoft QIO stack.

Use Case Pilots: Simulating for Logistics Giants

Two early-access logistics partners participated in simulation pilots in March 2021:

• FedEx Supply Chain: Modeled one of their Ohio distribution centers. The hybrid simulations identified a new bin-slotting strategy that reduced robotic congestion by 14%, increasing hourly throughput.

• Cainiao (Alibaba Logistics): Ran a simulation of an automated warehouse in Wuxi, China. Quantum optimization improved AGV task distribution, reducing path overlap and idle time by over 11% during peak demand scenarios.

Both companies contributed anonymized warehouse layouts to further refine the system’s ability to generalize across facility types.

Microsoft and Honeywell: A Quantum-Industrial Alliance

Honeywell and Microsoft had previously collaborated in 2020 to make Honeywell’s quantum hardware available via Azure Quantum. This warehouse project marks a step beyond access—it’s joint domain-specific co-development of logistics software powered by quantum computing.

The collaboration also leverages:

• Microsoft’s Q# programming tools for quantum model development

• Honeywell’s historical process automation expertise to fine-tune warehouse dynamics modeling

• Azure’s cloud infrastructure for global scalability and storage

Broader Impact on Quantum Logistics

This initiative signals growing momentum in applying quantum computing to real-world industrial logistics, with warehousing emerging as a fertile testing ground due to:

• High combinatorial complexity

• Need for real-time control

• Tightly coupled hardware-software environments

It also complements trends such as:

• The rise of warehouse robotics fleets (e.g., Locus, GreyOrange)

• Micro-fulfillment centers needing ultra-efficient layouts

• Sustainability demands driving energy-optimized routing

Technical Highlights from March 2021 Trials

Simulation runs from the March test window showcased:

• 17% improvement in picking throughput under peak loads in a three-zone warehouse layout

• 11% reduction in AGV collision incidents through optimized path scheduling

• Identification of alternative layouts that reduced aisle congestion by 9% without added hardware

These results were validated across classical and quantum-hybrid versions of the model, confirming both feasibility and advantage in specific planning scenarios.

Roadmap: From Simulation to Live Integration

Looking beyond the March launch, the roadmap includes:

• Integration with real-time WMS (Warehouse Management Systems) like Manhattan Associates and SAP EWM

• Live pilot deployments with autonomous robot fleets in North America and East Asia

• Support for quantum-secure communications via Azure Quantum encryption modules

Honeywell also indicated future upgrades to the platform’s compatibility with 5G and edge computing nodes, enabling near-real-time quantum optimization for active warehouse operations.

Challenges and Mitigations

While the technology showed promise, March trials surfaced several limitations:

• Limited quantum hardware scale constrained the complexity of real-time scenarios

• Data encoding overhead in translating simulation parameters into QUBO formats added latency

• Skills gap among warehouse IT personnel to configure and interpret hybrid simulation results

To mitigate these, Microsoft launched new documentation for warehouse operators and offered co-development sprints with selected partners through the Azure Quantum Innovation Hub.

Strategic Implications for Supply Chains

By enabling more efficient warehouse layout planning, robotic coordination, and inventory movement, this simulation tool could become a foundational technology for:

• Just-in-time inventory ecosystems

• Ultra-fast e-commerce fulfillment

• Green logistics through reduced energy consumption

Moreover, it positions quantum simulation as a decision support layer, not just a research tool—making quantum value tangible even at current hardware limitations.

Fujitsu Brings Quantum-Inspired Logistics Optimization to Southeast Asia

In February 2021, Fujitsu announced the expansion of its Digital Annealer-based logistics optimization platform into Southeast Asia, partnering with regional logistics firms and multinationals to improve route planning, warehouse slotting, and cross-border delivery efficiency. While not a pure quantum computer, the Digital Annealer leverages quantum-inspired techniques to tackle combinatorial logistics challenges at scale—bringing near-term performance gains to emerging markets with complex, fragmented supply chains.

As congestion, labor disruptions, and trade volatility continue to test supply chain resilience in Southeast Asia, Fujitsu's expansion reflects growing interest in using quantum-inspired algorithms to navigate logistical uncertainty.

What Is Fujitsu’s Digital Annealer?

Fujitsu’s Digital Annealer is a quantum-inspired computing architecture that mimics aspects of quantum annealing—a method for finding the global minimum of complex optimization problems. Unlike quantum annealers like D-Wave’s systems, the Digital Annealer runs on classical hardware but is built to solve Quadratic Unconstrained Binary Optimization (QUBO) problems—many of which are at the heart of logistics operations.

In logistics, such QUBO problems include:

• Vehicle routing with time windows (VRPTW)

• Dynamic warehouse slotting

• Container and pallet loading optimization

• Network flow scheduling

The Digital Annealer offers near-real-time optimization at a scale and speed that traditional solvers struggle to match, especially in dense or constraint-heavy networks.

Why Southeast Asia?

Fujitsu's decision to target Southeast Asia for early expansion was based on both logistical need and digital opportunity. The region is characterized by:

• Archipelagic geography (Indonesia, Philippines): making route planning highly variable and multimodal.

• Emerging e-commerce boom: particularly in Vietnam, Malaysia, and Thailand, which require faster fulfillment optimization.

• Intra-ASEAN trade growth: increased cross-border freight needs across Singapore, Malaysia, Cambodia, and Thailand.

• Logistics infrastructure gaps: where quantum-inspired algorithms can compensate for infrastructure inefficiencies by maximizing route and resource utilization.

By targeting this region, Fujitsu positioned its Digital Annealer as a tool for leapfrogging traditional logistics bottlenecks, rather than merely optimizing mature systems.

Key Partnership: Thai Logistics Pilot with SCG Logistics

One of the first commercial deployments of the Digital Annealer in Southeast Asia was a joint pilot with SCG Logistics, the supply chain subsidiary of Thailand’s Siam Cement Group (SCG).

Objectives of the Pilot:

• Optimize last-mile delivery for construction and consumer goods across Bangkok and outlying provinces.

• Reduce fuel usage and delivery times through multi-constraint route optimization.

• Test performance under urban congestion, driver time windows, and variable delivery volumes.

Pilot Architecture:

1. Data Integration: Order data, vehicle locations, customer constraints, and real-time traffic updates fed into the Digital Annealer’s API.

2. QUBO Modeling: Fujitsu’s platform modeled the vehicle routing problem (VRP) with time window and capacity constraints.

3. Solver Output: In under 30 seconds, the system generated route configurations that balanced driver workload, vehicle utilization, and on-time performance.

Results:

• Delivery time improvement of 13% on average across tested regions.

• Vehicle fleet reduction of 6% without affecting delivery coverage.

• Fuel savings of approximately 8.5%, contributing to SCG’s ESG targets.

According to SCG Logistics’ CIO, the pilot “demonstrated that quantum-inspired logistics can deliver tangible ROI today, not just theoretical gains.”

Use Case: Cross-Border Truck Routing between Singapore and Malaysia

Another notable application was in cross-border freight planning between Singapore and Johor Bahru, Malaysia, where customs wait times, route choices, and driver hours-of-service (HOS) constraints introduce complexity.

Fujitsu collaborated with a regional 3PL to:

• Encode border crossing windows and delays into the optimization framework.

• Model truck rest period requirements and slot-based loading schedules.

• Generate optimized sequences for pickups and deliveries across the border, reducing idle time and bottlenecks.

This use case demonstrated the Digital Annealer’s suitability not just for urban distribution but for multi-jurisdictional logistics—where regulations, tolls, and real-time delays must all be harmonized.

Comparative Advantage Over Classical Methods

While the Digital Annealer runs on classical infrastructure, it brings meaningful advantages over traditional solvers for certain logistics applications.

Feature

Traditional Solvers

Digital Annealer

Solve Time (Complex VRP)

Minutes to hours

Seconds to sub-minute

Scalability (Nodes/Variables)

Limited by heuristics

Thousands of variables

Constraint Flexibility

High with expert tuning

Naturally embedded in QUBO

Re-optimization Speed

Slow to adapt in real time

Supports dynamic re-solving

Hardware Requirements

Server farms or HPC

Runs on compact classical servers

For logistics providers in emerging markets, the key appeal is performance without needing quantum hardware, allowing for scalable deployment today.

Strategic Implications for Logistics in Emerging Markets

Southeast Asia’s logistics sector is undergoing rapid digitalization. With the rise of AI-powered visibility platforms, real-time delivery tracking, and smart warehousing, optimization becomes a critical bottleneck. Fujitsu’s Digital Annealer can serve as a plug-in optimization layer for:

• Transportation Management Systems (TMS)

• Warehouse Management Systems (WMS)

• Supply Chain Control Towers

Fujitsu also hinted at future cloud-native integrations, allowing logistics providers to run quantum-inspired optimization jobs from within existing ERP systems like SAP or Oracle.

Regional Expansion and Next Steps

Following February 2021’s initial pilot results, Fujitsu outlined its roadmap for further expansion in Southeast Asia:

• Philippines: Exploring urban logistics applications with partners in Metro Manila and Cebu.

• Vietnam: Targeting port-to-warehouse routing improvements near Hai Phong and Ho Chi Minh.

• Indonesia: Collaborating with e-commerce fulfillment providers to optimize island-hopping delivery schedules.

The company also announced partnerships with several ASEAN governments and trade bodies to develop training modules and sandbox environments where logistics professionals can learn to formulate optimization problems using QUBO techniques.

Global Alignment and Competitive Landscape

Fujitsu’s approach is part of a broader global movement to bring quantum or quantum-inspired optimization into operational settings ahead of scalable quantum hardware.

Competitors include:

• D-Wave: Commercial quantum annealers applied to air cargo and intermodal routing.

• Zapata Computing: Hybrid quantum algorithms targeting pharmaceutical and retail logistics.

• Multiverse Computing: Piloting quantum finance and logistics optimization in the EU and Latin America.

By emphasizing deployment-readiness, Fujitsu has carved a niche for organizations seeking performance gains without hardware constraints or steep learning curves.

Conclusion: A Quantum-Inspired Step Toward Smarter Supply Chains

Fujitsu’s expansion into Southeast Asia in February 2021 marked a practical milestone in the evolution of quantum-inspired logistics. In a region where infrastructure and demand patterns are in constant flux, the ability to dynamically optimize routes, resources, and schedules is not just valuable—it’s essential.

By combining the mathematical rigor of quantum annealing with the accessibility of classical hardware, the Digital Annealer offers emerging markets a powerful tool to improve efficiency, reduce emissions, and enhance delivery performance—all without waiting for full-fledged quantum computers to mature.

This move may signal a broader trend: the rise of quantum-inspired logistics platforms as a bridge technology that delivers measurable results today while preparing the groundwork for quantum-native supply chains tomorrow.

Logistics in Orbit: A New Quantum Frontier

In February 2021, NASA, AWS (Amazon Web Services), and quantum software firm QC Ware unveiled the results of a collaborative research project aimed at optimizing satellite cargo routing and orbital logistics using quantum-inspired and hybrid quantum algorithms.

The joint effort explored how next-generation quantum algorithms could be applied to low Earth orbit (LEO) satellite networks—particularly those responsible for coordinating orbital servicing, cargo transfers, and satellite-to-satellite communications.

As space logistics rapidly evolves with the rise of mega-constellations (e.g., SpaceX Starlink, OneWeb) and commercial space stations (e.g., Axiom Space), the ability to dynamically manage orbital assets is becoming a core logistical challenge. This project represented one of the first public attempts to use quantum computing principles to address that challenge.

The Partners and Their Roles

NASA Ames Research Center

NASA Ames has long been a hub for research at the intersection of computing and aerospace. The agency’s Advanced Supercomputing Division and Intelligent Systems Division led the formulation of orbital cargo logistics problems as discrete optimization tasks.

QC Ware

A Silicon Valley-based startup, QC Ware develops quantum algorithms that run on both current quantum processors and classical simulators. Their forte lies in hybrid quantum-classical optimization, offering enterprise users a bridge between today’s NISQ (Noisy Intermediate-Scale Quantum) systems and future fault-tolerant quantum platforms.

Amazon Web Services (AWS)

Through its Amazon Braket platform, AWS provided quantum computing access and infrastructure to host the simulations, run hybrid workloads, and benchmark classical vs. quantum performance across algorithms.

Problem Statement: Routing and Resource Allocation in Orbit

Satellites in low Earth orbit operate in dynamic, resource-constrained environments. Whether it’s transferring cargo between autonomous servicing satellites, allocating bandwidth among communications nodes, or planning refueling operations for orbital tugs, space logistics demands multi-variable, real-time decision-making.

Key constraints include:

• Orbital mechanics: Timing and positioning windows for rendezvous operations.

• Fuel budgets: Limited delta-v for maneuvering satellites or service drones.

• Communication bandwidth: Prioritization of high-value data during congestion.

• Safety: Avoidance of potential conjunctions or routing overlaps.

Primary Optimization Goals

NASA and QC Ware focused on a particular use case: inter-satellite cargo or resource transfers in a simulated network of autonomous servicing vehicles and target satellites. The goal was to:

• Minimize total fuel expenditure.

• Maximize total cargo throughput across the constellation.

• Avoid conflicts in orbital paths or service schedules.

Translating Space Problems into Quantum Language

To tackle these objectives, the team modeled the orbital cargo routing challenge as a variant of the Vehicle Routing Problem (VRP)—a classic NP-hard problem in logistics. In this setting, each servicing satellite is a “vehicle,” each cargo opportunity is a “customer,” and the constraints include orbital dynamics and resource availability.

QC Ware then reformulated this as a Quadratic Unconstrained Binary Optimization (QUBO) problem—a structure well-suited to quantum annealers and variational quantum algorithms (VQAs).

Techniques Used:

• Quantum Approximate Optimization Algorithm (QAOA): A gate-based algorithm used for discrete optimization on near-term quantum computers.

• Quantum-inspired classical solvers: Leveraged tensor networks and simulated annealing to benchmark performance against quantum runs.

• Hybrid solvers: Combined QAOA layers with classical post-processing to refine suboptimal solutions in real-time.

By comparing pure classical, hybrid, and quantum approaches, the team aimed to understand when quantum methods might offer a speedup or accuracy advantage.

Key Findings and Performance Insights

In February 2021, the collaborators released results from simulation trials involving up to 40 satellite nodes and 150+ cargo transfers.

Early Findings:

• Solution Quality:
  Hybrid quantum-classical methods generated transfer routes within 3–5% of optimality compared to exhaustive classical solvers, but in up to 80% less computation time.

• Scalability:
  Quantum-inspired solvers scaled well with network size, outperforming greedy heuristics on denser orbital graphs.

• Conflict Avoidance:
  The QUBO structure allowed for effective encoding of mutual exclusivity constraints, improving safety in orbital path planning.

• Real-time Adjustability:
  Variational algorithms allowed for re-optimization under changing conditions (e.g., satellite outages or weather delays) without re-running the full model.

These findings suggested that quantum tools—especially hybrid and inspired algorithms—could soon play a role in autonomous orbital logistics, where dynamic re-planning is essential.

Strategic Implications for Aerospace Logistics

As orbital logistics becomes more commercialized, tools that enhance operational flexibility, fuel efficiency, and scheduling accuracy are vital. NASA’s exploration of quantum logistics models with QC Ware signals that the agency is preparing for next-gen supply chains beyond Earth.

Potential Applications:

• On-Orbit Servicing:
  Routing robotic maintenance or refueling drones to satellites in need of repair.

• Space-Based Manufacturing:
  Scheduling cargo pickups and drop-offs between orbital factories and collection nodes.

• Lunar Logistics Precursor:
  Validating algorithms for future Artemis mission supply networks connecting lunar orbit, gateway stations, and surface modules.

• Constellation Management:
  Dynamic bandwidth and compute resource allocation across hundreds of LEO satellites.

NASA sees quantum optimization as a way to enhance the autonomy and resilience of these complex systems.

A Glimpse into the Hybrid Future

While current gate-based quantum computers remain limited in scale and noise resilience, the project underscored the near-term power of hybrid models. These allow today's logistics teams to:

• Frame their problems using quantum-native abstractions (e.g., QUBO).

• Run on simulators or quantum-inspired engines for near-optimal solutions.

• Transition seamlessly to real quantum backends as hardware matures.

AWS’s Amazon Braket played a critical role in testing across platforms, including Rigetti’s superconducting chips and IonQ’s trapped-ion systems. QC Ware’s platform handled the abstraction layer, ensuring that orbital routing models could be deployed across any backend.

Policy and Industry Alignment

This February 2021 demonstration aligns with broader policy shifts:

• NASA’s embrace of commercial logistics through public-private partnerships.

• U.S. national quantum initiatives, including the National Quantum Coordination Office and QIS Working Group.

• Private sector push toward quantum commercialization in aerospace from companies like Lockheed Martin, Airbus, and SpaceX.

The project also feeds into a growing body of quantum-readiness research that seeks to equip industries with modular, algorithm-agnostic quantum workflows.

Conclusion: Quantum Logistics for the Final Frontier

The collaboration between NASA, QC Ware, and AWS in February 2021 set a high-water mark for quantum logistics research in aerospace. By successfully modeling and solving satellite cargo routing problems using quantum-inspired and hybrid quantum techniques, the project showcased how future space missions could leverage quantum-enhanced autonomy.

As commercial space operations expand—from in-orbit manufacturing to lunar resource extraction—dynamic, resource-efficient logistics systems will be essential. This effort demonstrates that quantum computing is not just a laboratory curiosity—it’s an emerging toolset for solving humanity’s next-generation supply chain problems… even in orbit.

Quantum Inventory Optimization Moves Into Grocery Supply Chains

In February 2021, Canadian quantum computing pioneer D-Wave Systems and regional grocery retailer Save-On-Foods launched a collaborative pilot to test quantum optimization techniques on inventory logistics. The goal: to tackle challenges in stock placement, replenishment cycles, and shelf space allocation across Save-On-Foods' extensive Western Canada retail network.

This marked one of the earliest known efforts to bring quantum computing into mainstream grocery logistics—a sector known for razor-thin margins, complex perishability constraints, and highly dynamic demand signals.

While D-Wave’s quantum annealing systems had previously been applied to airline scheduling and pharmaceutical R&D, this project aimed to push their application into the world of consumer-facing supply chains, where real-time optimization has direct effects on business performance and customer satisfaction.

The Canadian Quantum-Grocery Alliance: Who's Involved?

D-Wave Systems

Based in Burnaby, British Columbia, D-Wave is a global leader in quantum annealing technology, offering cloud-based access to quantum systems optimized for combinatorial optimization problems. In 2020, the company launched its Advantage™ system, which boasts 5000+ qubits and supports complex problem embeddings on large graph topologies.

Save-On-Foods

A division of the Jim Pattison Group, Save-On-Foods operates more than 170 stores across British Columbia, Alberta, Saskatchewan, Manitoba, and Yukon. With a strong focus on regional sourcing, frequent promotional cycles, and high customer expectations for fresh produce, the chain presents a uniquely challenging optimization landscape.

The collaboration was facilitated by Canada's Digital Technology Supercluster, an innovation consortium helping deploy advanced technologies into critical sectors.

The Optimization Challenge: Beyond Forecasting

Grocery logistics is a complex balancing act involving perishable goods, regional preferences, seasonal fluctuations, supplier constraints, and in-store handling capacity. While many retailers have adopted machine learning for demand forecasting, that is only one part of the operational puzzle.

D-Wave and Save-On-Foods focused instead on the next frontier: real-time inventory reallocation and restocking optimization.

Key Problem Areas Explored:

1. Shelf Replenishment Cycles
   Deciding the most efficient replenishment timing across hundreds of stores, while minimizing waste and labor costs.

2. Stock Redistribution Between Stores
   Rebalancing items across locations based on local demand fluctuations and short-term sales events.

3. In-Store Routing for Stocking Staff
   Minimizing walking time and inefficiencies for workers restocking shelves—particularly important during peak hours.

4. Product Placement Trade-offs
   Choosing which SKUs to prioritize on limited shelf space using real-time pricing, inventory, and supplier data.

These challenges share a common mathematical structure: combinatorial optimization under multiple constraints, making them ideal candidates for quantum annealing solutions.

How Quantum Annealing Was Applied

D-Wave’s team translated logistics problems into Quadratic Unconstrained Binary Optimization (QUBO) models—the native format for D-Wave’s annealing machines. These QUBO formulations allow complex decision spaces (e.g., "if SKU X is placed in location A, do not place SKU Y there") to be embedded into the quantum system’s energy landscape.

Process Steps:

1. Data Preprocessing
   Save-On-Foods provided anonymized historical inventory, sales, and routing data.

2. Problem Formulation
   Specific logistics challenges were modeled as constrained optimization problems. For example, a shelf replenishment problem might involve selecting an optimal combination of restocking tasks for a time-limited shift window.

3. Hybrid Solver Usage
   D-Wave deployed its hybrid quantum-classical solvers, using the Advantage system in tandem with classical heuristics to efficiently solve problems too large for pure quantum processing.

4. Scenario Testing
   Simulations were run across multiple store configurations, from large urban locations to smaller remote ones, to validate the robustness of the models.

Key Results from the February 2021 Pilot

By the end of the initial trial in February 2021, D-Wave and Save-On-Foods shared preliminary findings indicating tangible performance improvements.

Early Performance Metrics:

• Inventory Redistribution Efficiency:
  A 14% reduction in inter-store transfer volume while meeting the same customer demand profiles.

• Shelf Replenishment Timing:
  9–12% improvement in labor utilization for restocking tasks, measured as reduction in unnecessary shelf visits during shifts.

• Stockout Risk Management:
  Better prioritization of restocking tasks led to a 6% drop in SKU-level stockouts during promotional periods.

• Computation Time:
  Quantum-enhanced hybrid solvers delivered solutions for daily scheduling problems up to 50x faster than legacy heuristic-based tools.

While the project did not yet extend to full-scale live operations, the simulation and planning tools demonstrated clear logistical advantages, especially for high-velocity and seasonal SKUs.

Broader Significance: Quantum Grocery as a Sectoral Test Case

This initiative was more than a one-off pilot. It demonstrated how quantum computing could become integral to day-to-day business operations in consumer retail environments.

Why Grocery Logistics Matters for Quantum:

• Fast Turnover:
  Grocery chains operate on tight restocking cycles, making even small gains in efficiency impactful.

• Data Rich, Constraint Heavy:
  SKU-level forecasting, freshness requirements, and dynamic pricing create complex optimization environments ripe for quantum methods.

• Immediate ROI:
  Unlike pharmaceuticals or defense sectors where quantum payoffs may be long-term, grocery chains can see short-term bottom-line benefits from optimization.

This means that grocery logistics offers an ideal proving ground for real-world hybrid quantum applications.

Strategic Impact for D-Wave and Canada’s Quantum Sector

The pilot helped position D-Wave not just as a quantum hardware manufacturer, but as a solutions provider for industry-scale logistics challenges. It also contributed to Canada’s broader effort to stay competitive in the global quantum race.

Notable Outcomes:

• D-Wave gained deeper insights into enterprise-ready hybrid workflows for optimization under real-world constraints.

• Save-On-Foods gained a competitive edge in regional logistics efficiency, a differentiator in markets with high retail competition.

• The project served as a case study for Canada’s Digital Technology Supercluster, showcasing how public-private R&D collaboration can unlock economic value.

Next Steps and Outlook

Following the February 2021 pilot, the partners explored avenues for scaling and refining their approach.

Planned Next Steps:

• Integration into Live Planning Systems:
  Connecting quantum optimization tools directly to Save-On-Foods’ existing inventory and logistics platforms.

• Expansion to Vendor Supply Chains:
  Applying quantum models to upstream supply issues—e.g., selecting suppliers based on delivery windows and spoilage risk.

• Sectoral Rollout:
  Extending the pilot framework to other grocery chains or pharmacy distribution networks, where shelf-space optimization is similarly critical.

• Workforce Upskilling:
  Training operations and IT staff to interpret quantum-generated insights using graphical dashboards and confidence level indicators.

Conclusion: Toward Quantum-Driven Retail Logistics

The February 2021 pilot between D-Wave and Save-On-Foods represented a bold step toward integrating quantum technology into everyday logistics. With clear early wins in replenishment efficiency, inventory accuracy, and decision speed, this effort illustrated how quantum annealing can create real value in high-volume, time-sensitive sectors like grocery retail.

As Canada’s quantum ecosystem continues to evolve and commercial interest in applied quantum computing grows, such pilots serve not just as technical milestones—but as blueprints for real-world digital transformation powered by quantum science.

This case adds weight to the broader realization: quantum logistics is not a speculative future—it's already underway, shelf by shelf.

Quantum Logistics in the Heart of Tokyo

As e-commerce demand in Japan surges, urban logistics networks are under growing pressure to deliver parcels faster, more accurately, and with fewer emissions. In response, NEC and Japan Post launched a pilot program exploring how quantum computing could address these challenges in the highly congested Tokyo metro area.

The project uses NEC’s quantum annealing-based optimization engine to perform real-time routing simulations for parcel delivery vehicles across selected wards in central Tokyo. The focus is on modeling complex delivery scenarios where traditional route-planning tools struggle with efficiency and adaptability.

Technical Highlights of the Pilot

The feasibility study focuses on optimizing three key components of last-mile logistics:

• Dynamic route recomputation to adjust for traffic, weather, and delivery cancellations

• Parcel clustering algorithms that assign packages to delivery zones using real-time density and volume data

• Driver load balancing to ensure equitable and efficient distribution of parcel volumes across personnel

NEC’s quantum-inspired optimization tool translates these logistics constraints into QUBO (Quadratic Unconstrained Binary Optimization) problems, allowing it to find better-than-classical solutions in tight timeframes.

Simulation Areas and Scope

During February 2021, simulations were conducted on live datasets covering Tokyo’s Chiyoda, Shibuya, and Bunkyo wards—zones known for high population density and complex delivery patterns. NEC and Japan Post used:

• Live GPS and delivery point data from Japan Post’s parcel fleet

• Traffic congestion patterns obtained from city sensors and Google API feeds

• Workforce scheduling datasets to model handoffs, breaks, and shifts

Measurable Improvements in Routing Efficiency

Early results from February’s feasibility study showed several promising metrics:

• 13% reduction in total delivery distance, thanks to quantum-enhanced route clustering

• 11% better on-time delivery performance under peak load scenarios

• Reduced backtracking and idle time by optimizing package-to-route matching

These outcomes point to potential fuel savings, faster delivery turnarounds, and improved worker satisfaction in high-volume zones.

Strategic Rationale for Quantum Testing

Japan Post's involvement is part of its medium-term logistics modernization strategy, which includes digital twins, AI forecasting, and smart routing. By adding quantum computing to this stack, Japan Post aims to future-proof its capabilities as parcel volumes continue rising due to e-commerce and aging demographics.

For NEC, the collaboration offers a valuable real-world testing ground for its quantum-inspired technology platform. While Japan's homegrown quantum processors are still in early stages, NEC's hybrid architecture bridges the gap by using classical systems designed to simulate quantum behavior efficiently.

Government and Ecosystem Support

This pilot aligns with broader Japanese national initiatives:

• Japan’s Moonshot R&D Program (Goal 6), which promotes quantum breakthroughs for societal infrastructure

• METI’s Logistics DX Strategy, encouraging public-private partnerships in next-generation supply chain technologies

• Support from RIKEN’s quantum software community, where NEC is an active member

Together, these efforts signal a coordinated push to embed quantum solutions in industrial contexts, starting with transportation and logistics.

Looking Ahead: Toward Deployment

If feasibility testing continues to show positive results, NEC and Japan Post plan to expand the quantum routing trial to include:

• Suburban and peri-urban delivery zones in Saitama and Kanagawa

• Multi-modal handoffs, integrating bike and drone couriers

• Cold-chain deliveries where timing precision is critical

By mid-2022, NEC hopes to integrate its quantum engine into Japan Post’s backend delivery management system, creating one of the first hybrid-quantum production deployments in Asia’s postal sector.

Implications for Global Logistics Players

This Tokyo-based pilot provides a roadmap for other dense urban regions looking to:

• Tackle delivery inefficiencies under rising parcel loads

• Reduce environmental impact through optimized routing

• Integrate quantum technology with existing logistics tech stacks

As NEC continues refining its platform and Japan Post scales its trials, their results may influence how cities like Seoul, New York, and London approach quantum logistics in the coming years.

Conclusion: Quantum Delivery Optimization in Motion

NEC and Japan Post’s February 2021 pilot represents a meaningful early application of quantum optimization in real-world logistics. By applying quantum-inspired technologies to complex routing and delivery challenges in one of the world’s most densely populated urban areas, the two partners are testing the boundaries of what next-generation computation can deliver for society.

While still in the feasibility stage, the measurable gains in efficiency, accuracy, and responsiveness indicate that hybrid quantum solutions may soon have a tangible role in last-mile delivery operations. As the technology matures and Japan’s broader quantum ecosystem develops, projects like this one could redefine urban logistics not only in Tokyo but across global megacities facing similar challenges.

With NEC’s platform evolving and Japan Post preparing for broader implementation, the foundation is being laid for scalable, secure, and sustainable quantum-driven logistics—making the promise of quantum computing a visible part of everyday infrastructure.