Air Canada Cargo and D-Wave Launch Quantum Logistics Feasibility Study
July 6, 2020
Aviation Turns to Quantum in a Time of Crisis
By mid-2020, the aviation industry was in disarray due to the global COVID-19 pandemic. Passenger flights were grounded en masse, yet cargo demand surged—especially for medical supplies and e-commerce goods. Amid this disruption, Air Canada Cargo began exploring how quantum computing might help them better match constrained capacity with unpredictable, dynamic demand.
The carrier engaged Burnaby-based D-Wave Systems to launch a joint feasibility study focused on quantum optimization of air cargo logistics—with a particular focus on constrained routing, volumetric capacity, and resource allocation at major Canadian airports like Toronto Pearson and Vancouver International.
Why Quantum for Air Freight?
Air freight logistics involve a staggering number of variables: volumetric space vs. weight, scheduling of limited aircraft slots, customs delays, ground handling, and multi-leg international routing. Classical optimization struggles with the combinatorial complexity, especially when real-time re-planning is needed due to weather, delays, or sudden demand shifts.
Quantum computing—especially the quantum annealing architecture used by D-Wave—offers promise for such combinatorial problems.
Key areas explored in the study included:
Cargo load optimization: Matching cargo type, weight, and volume to aircraft and container constraints using quantum-enhanced packing models.
Routing with constraints: Finding optimal or near-optimal routing schedules for multi-leg cargo itineraries considering aircraft type, crew availability, and slot restrictions.
Gate and handling resource scheduling: Assigning ground crews, tarmac equipment, and hangar slots efficiently at high-traffic times.
Project Structure and Goals
The July 2020 collaboration focused on building QUBO (Quadratic Unconstrained Binary Optimization) models of simplified but realistic Air Canada Cargo operations. D-Wave used its Leap cloud platform to run early-stage experiments on its 2000Q quantum annealer, later transitioning to the then-new Advantage system, which had 5000+ qubits.
The project aimed to:
Evaluate whether quantum optimization could produce improvements over Air Canada’s classical heuristics and planning software.
Measure solution quality and processing time on quantum hardware vs. traditional solvers under time constraints.
Create decision-support models that could be integrated into dispatch planning systems at cargo operations control centers.
Early Results: Promising but Nascent
While detailed technical results were not made public, insiders familiar with the project reported:
A 3–5% improvement in load utilization for certain high-volume lanes using quantum-generated packing solutions.
Modest but consistent reductions in gate assignment conflicts during high-traffic periods.
Valuable insights into problem pre-processing and QUBO formulation that informed later phases of the project.
However, challenges were also noted:
Quantum hardware limitations required significant problem simplification and hybridization with classical solvers.
Near-optimal solutions needed careful calibration of annealing parameters and penalty weightings.
Despite these limitations, the study concluded that quantum tools could serve as a “co-processing layer” to support dispatchers and planners—offering high-quality alternatives within seconds when re-routing was needed due to disruption.
Global Relevance: Other Airlines Taking Notice
While Air Canada Cargo’s quantum exploration may have been one of the earliest public aviation quantum optimization efforts, it fits into a larger trend. In 2020:
Lufthansa Systems began assessing quantum algorithms for crew scheduling.
Airbus's Quantum Computing Challenge (AQC) advanced finalists testing route optimization and structural load simulations.
Boeing maintained interest in quantum-safe communications and optimization, with ties to IBM Q and the University of Washington.
Quantum Optimization vs. Traditional Heuristics
A key focus of the Air Canada–D-Wave study was to determine where quantum offered a tangible operational edge over traditional methods like:
Simulated annealing
Genetic algorithms
Integer linear programming
Results showed that for certain high-complexity, low-latency problems (e.g., re-routing during disruptions or last-minute cargo additions), quantum-generated alternatives outperformed heuristics in decision quality. However, for predictable baseline planning, traditional systems remained sufficient.
This highlighted the value of hybrid systems, where quantum solvers act as rapid contingency planners or “what-if” scenario engines.
Integration and Next Steps
The study concluded in late Q3 2020, with the following recommendations:
Develop a hybrid planning module that leverages quantum solvers selectively for complex or time-sensitive decisions.
Expand the scope to multi-airport, multi-day cargo scheduling.
Evaluate use of D-Wave’s newer Advantage processor for larger problem graphs.
Air Canada Cargo indicated interest in pursuing a pilot phase, contingent on improved API integration with their existing logistics planning software.
Conclusion: From Disruption to Innovation
The COVID-19 pandemic forced the global aviation industry to rethink resilience, flexibility, and decision-making under uncertainty. For Air Canada Cargo, this meant experimenting with one of the most advanced computational paradigms available.
While not ready for full deployment, the D-Wave collaboration proved that quantum optimization has a place in the future of air freight logistics, particularly when used as a tactical tool for solving tough, time-sensitive puzzles. As quantum hardware continues to mature, such feasibility studies lay the groundwork for broader operational transformation.
This July 2020 milestone not only marked a first for Air Canada Cargo, but also contributed to the global push toward quantum-enhanced decision-making in supply chain management.