Airbus and Atos Explore Quantum Simulation to Streamline Aerospace Logistics
July 20, 2017
Airbus Turns to Quantum Simulation for Aerospace Logistics Efficiency
In a landmark move that could reshape aerospace supply chains, Airbus and Atos announced on July 20, 2017, a strategic collaboration to explore the application of quantum simulation for improving logistical efficiency in aircraft manufacturing and global parts distribution. This partnership marks one of the earliest crossovers between quantum computing R&D and the aerospace sector’s supply chain digitization efforts.
With over 12,000 suppliers across 100 countries and manufacturing lead times stretching into months, Airbus's logistics ecosystem represents one of the most complex in the industrial world. Managing such an operation demands not just traditional enterprise resource planning (ERP) systems, but simulation tools capable of analyzing millions of interdependent variables—a task quantum simulation is uniquely suited for.
Atos’s QLM at the Core of the Collaboration
At the center of this effort is Atos’s Quantum Learning Machine (QLM), a high-performance simulator capable of emulating quantum algorithms on classical supercomputers. Unlike a fully-fledged quantum computer, which is still several years from commercial viability, QLM enables industries to test quantum models today, even without quantum hardware.
This gives Airbus the ability to begin developing quantum-native supply chain optimization models—such as inventory simulations, dynamic part routing, and demand forecasting—well ahead of general quantum deployment.
“We need to understand how quantum will help optimize decision-making in large-scale, mission-critical environments,” said Thierry Breton, CEO of Atos at the time. “Airbus offers a unique testbed where quantum simulation could unlock real industrial value.”
Logistics Use Cases: Beyond Theoretical
The use cases extend well beyond theoretical models:
Spare Parts Forecasting: Quantum simulations could optimize the storage and routing of replacement parts to global maintenance depots.
Material Sourcing: With procurement pathways exposed to geopolitical risk and supplier variability, quantum models can offer more robust sourcing strategies.
Aircraft Assembly Scheduling: Airbus’s multi-site production model, with parts assembled in Germany, France, Spain, and the UK, creates logistical complexities that traditional tools struggle to optimize under constraints.
The simulation capabilities of the Atos QLM allow Airbus to evaluate multiple logistics scenarios simultaneously, especially for just-in-time inventory processes.
Strategic Value in Aerospace Supply Chains
The aerospace industry faces unique challenges in logistics, from long certification cycles and tight production windows to heavy reliance on multimodal transport. With the A350 and A320neo programs reaching peak demand in 2017, Airbus’s need for predictive logistics tools was greater than ever.
Quantum simulation, when paired with classical systems, can model everything from vendor delay impacts to optimal loading of cargo on aircrafts for balancing fuel efficiency with weight distribution. These compound logistics problems are ideal candidates for hybrid quantum-classical optimization.
France and Europe's Quantum Ambitions
This collaboration also aligned with France’s broader national quantum strategy, which began taking shape in 2017 under the French Ministry for Higher Education, Research, and Innovation. Atos, headquartered in France, had positioned itself as a quantum leader, working closely with CNRS and INRIA on theoretical research, while bringing commercial offerings like QLM to market.
By collaborating with Airbus, Atos strengthened its role in integrating French quantum research with tangible industrial applications—particularly in sectors like defense, aerospace, and transport.
Aerospace as a Testbed for Quantum Scalability
Airbus’s long-term vision includes exploring quantum technologies across multiple domains:
Quantum Sensors for precision navigation in GPS-denied environments.
Quantum Cryptography for securing aircraft telemetry and data uplinks.
Quantum Machine Learning for predictive maintenance of complex systems.
The logistics pilot with Atos is the starting point of a broader roadmap. Airbus later became involved with the Airbus Quantum Computing Challenge (AQCC) in 2019, but this 2017 partnership with Atos was the initial foray into logistics-specific quantum applications.
Global Competition in Aerospace Quantum Adoption
Airbus’s move came as Lockheed Martin in the U.S. continued its work with D-Wave on quantum annealing use cases for aircraft logistics. Meanwhile, China’s CASI (Chinese Aeronautical Systems Institute) began researching quantum-enabled aviation scheduling systems in mid-2017.
The Airbus-Atos partnership helped position Europe as a serious player in aerospace quantum innovation, not just from a research standpoint, but with actionable logistics goals and industrial relevance.
Cautious Optimism and the Road Ahead
Despite its promise, quantum simulation for aerospace logistics still faces hurdles:
Lack of real quantum processors with high qubit counts
Uncertainty around scaling hybrid models
Cost of integrating quantum simulations with legacy ERP and SCM platforms
Still, Airbus’s proactive engagement in 2017 set the stage for future logistics innovation.
“Quantum will not be a silver bullet tomorrow,” noted Airbus’s then Chief Digital Officer Marc Fontaine, “but it’s vital we start building capability now, so when the hardware matures, we’re already fluent in quantum thinking.”
Conclusion
The July 2017 announcement of Airbus and Atos's collaboration around quantum simulation marked a pivotal moment in quantum logistics evolution. By leveraging Atos’s QLM, Airbus began laying the groundwork for quantum-enhanced supply chain simulation—a bold step in one of the world’s most demanding logistics sectors. As aerospace becomes more digitized and globally interdependent, quantum modeling may become essential to keeping complex manufacturing ecosystems efficient, secure, and resilient.