Cold Chain Quantum Breakthrough: Classiq and Intel Collaborate on Quantum Workflow Optimization in Perishable Goods Logistics
June 22, 2021
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:
Leverage Classiq’s synthesis platform to rapidly design quantum circuits tailored to cold chain logistics optimization.
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.