Airbus and QC Ware Collaborate on Quantum Optimization for Aerospace Logistics
June 17, 2021
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:
Aircraft Cargo Reconfiguration: Repacking and weight balancing for A350 air freight missions during COVID-era repurposing.
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.