D-Wave Launches Hybrid Quantum Routing Prototype for Canadian Cold Chain Logistics

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.