Lockheed Martin Begins Quantum Simulation of Aerospace Supply Chains Using D-Wave 2X
February 16, 2015
On February 16, 2015, Lockheed Martin—one of the world’s largest aerospace and defense contractors—announced a pioneering initiative to apply quantum annealing to aerospace supply chain simulation. The effort focused on exploring how quantum computing could improve planning, scheduling, and risk management in highly complex logistics networks. Lockheed Martin’s collaboration with the University of Southern California’s Quantum Computation Center (USC-QCC) enabled access to a D-Wave 2X quantum annealer, one of the most advanced commercial quantum systems available at the time, capable of over 1000 qubits.
Aerospace Supply Chain Complexity
Aerospace logistics represents a uniquely challenging environment. Supply chains must coordinate:
Thousands of engine and avionics components with precise tolerances.
Maintenance, Repair, and Overhaul (MRO) schedules for military and commercial aircraft.
Multitiered subcontractor networks spanning multiple countries.
Security and compliance verification for sensitive or classified parts.
Even minor disruptions at a single node—such as a delayed component shipment or customs holdup—can cause grounded aircraft, mission delays, or costly production penalties. Traditionally, logistics optimization relied on linear programming, Monte Carlo simulations, and heuristic scheduling. These methods often struggle with combinatorial explosion, particularly under real-time constraints or multiple interdependent objectives.
Lockheed Martin’s initiative aimed to address these challenges using quantum annealing, which can efficiently explore large solution spaces and identify optimal or near-optimal configurations that are computationally expensive for classical methods.
Quantum Annealing Applied to Supply Chain Problems
Lockheed’s research team translated supply chain challenges into Quadratic Unconstrained Binary Optimization (QUBO) formulations suitable for D-Wave’s architecture. Key focus areas included:
Repair Cycle Compression
Optimizing the placement and quantity of spare parts to minimize aircraft downtime.
Scheduling repair and replacement tasks to meet operational readiness requirements.
Dynamic Vendor Allocation
Selecting the optimal mix of suppliers in response to fluctuating lead times and component availability.
Balancing cost, reliability, and redundancy for critical supply nodes.
Cargo Routing and Delivery Scheduling
Identifying the lowest-risk shipment paths considering restricted airspace, geopolitical constraints, and weather uncertainty.
Integrating multi-modal transport options to improve delivery resilience.
By mapping these problems to the D-Wave 2X, Lockheed could leverage quantum annealing’s strength in searching combinatorial landscapes efficiently. The hybrid approach combined classical optimization tools with quantum subproblem solvers for targeted improvements.
Early Results and Operational Insights
While still experimental in 2015, Lockheed’s pilot tests demonstrated tangible benefits:
12–18% reduction in total replacement part inventory cost.
Faster convergence of constrained scheduling models, reducing computational runtime.
Improved capability for adaptive logistics, including near-continuous rescheduling of critical components.
Simulations were conducted in controlled test environments, not yet deployed across live manufacturing pipelines. Nevertheless, results provided evidence that quantum-enhanced models could augment classical logistics systems to achieve measurable operational efficiencies.
Collaboration with USC and Technical Infrastructure
The project leveraged USC’s expertise in quantum computation and error suppression. Key elements of the technical stack included:
D-Wave 2X quantum annealer, configured for multi-variable QUBO problems.
Lockheed Martin’s anonymized aerospace logistics datasets for supply, demand, and repair scheduling.
Custom middleware connecting classical operational research (OR) solvers to quantum annealing subroutines.
Dr. Daniel Lidar of USC, an expert in quantum error suppression, emphasized: “This collaboration represents a crucial test of quantum computing’s ability to solve commercially valuable problems in real-world, high-stakes logistics.”
Strategic Implications for Aerospace and Defense
Quantum optimization in aerospace logistics has implications beyond commercial efficiency:
Fleet readiness: Predictive modeling ensures critical aircraft are operational when needed.
Mission-critical spare part allocation: Optimal stock placement reduces the risk of grounded aircraft during military operations.
Adaptive logistics under constraints: Scenarios including wartime disruption, extreme weather, or unexpected supplier failure can be simulated and mitigated in advance.
The ability to model these complex interdependencies using quantum annealing may provide Lockheed Martin a strategic advantage over competitors in both commercial and defense sectors.
Industry Context and Comparative Initiatives
Lockheed Martin’s quantum logistics initiative was part of an emerging industry trend:
Boeing explored quantum algorithms for materials simulation and production optimization.
Raytheon investigated quantum-enhanced secure logistics data transmission.
DARPA funded projects on quantum resilience for battlefield logistics.
By 2015, the defense industry increasingly recognized quantum computing as a potential differentiator—capable of reducing logistics lag, improving fleet readiness, and enhancing mission adaptability.
Challenges and Limitations
Lockheed Martin identified several hurdles in applying quantum annealing:
Hardware limitations: D-Wave systems were not yet fully fault-tolerant, limiting problem size and accuracy.
QUBO pre-processing complexity: Translating real-world supply chain problems into QUBO form required extensive classical computation.
Integration with operational pipelines: Real-world supply chain disruptions still required human and classical oversight.
The hybrid approach—using classical systems for overall planning and quantum processors for local sub-optimization—was considered the most practical near-term solution.
Future Directions
Lockheed’s team projected several near-term objectives:
Expansion of QUBO modeling to incorporate more detailed MRO schedules.
Testing quantum-assisted logistics in simulated operational environments.
Integration with automated control systems for inventory and transport monitoring.
Exploration of error mitigation techniques and scalable quantum-classical hybrid workflows.
As quantum hardware improves in qubit count, coherence, and error correction, the team anticipated broader performance gains and more direct applications in real-world aerospace operations.
Conclusion
Lockheed Martin’s February 16, 2015, initiative to simulate aerospace supply chain workflows using the D-Wave 2X quantum annealer represented a pioneering step in quantum-enabled logistics optimization.
While not yet fully operational, the pilot demonstrated that quantum annealing could:
Reduce inventory costs.
Accelerate constrained supply chain computations.
Support adaptive logistics planning in complex, security-sensitive environments.
By combining classical operational research tools with emerging quantum hardware, Lockheed Martin set a precedent for future aerospace and defense logistics innovation. The work highlighted the potential for quantum computing to become a foundational capability in 21st-century aerospace supply chains, enhancing efficiency, readiness, and resilience.