Davidson and Strangeworks have launched a collaboration to test hybrid quantum and classical optimization algorithms for military logistics and resource allocation, benchmarking their performance against established classical methods in operational scenarios
Davidson, an aerospace and defense engineering contractor, and Strangeworks, a quantum orchestration platform provider, have announced a technical partnership aimed at evaluating hybrid quantum and quantum-inspired algorithms for defense mission planning. The collaboration, revealed at the 2026 Space and Missile Defense Symposium in Huntsville, Alabama, focuses on applying advanced computational methods to optimize logistics, resource allocation, and operational readiness for military applications. The companies plan to benchmark these approaches against conventional high-performance classical algorithms to assess whether quantum or hybrid methods can deliver measurable improvements in real-world defense scenarios.
Technical Approach
The initiative will use Strangeworks' heterogeneous computing platform to integrate quantum, quantum-inspired, and classical solvers. Davidson brings three decades of experience in missile defense, electronic warfare, and cyber engineering, while Strangeworks contributes expertise in orchestrating diverse computational resources. Over the coming weeks, the partners will launch a proof-of-concept phase, formulating operationally relevant problem sets and running comparative benchmarks. The goal is to determine whether hybrid quantum-classical workflows can outperform legacy classical optimization in tasks such as supply chain management and tactical resource deployment.
Benchmarking and Evaluation
While the companies have not disclosed specific hardware platforms or qubit counts, the benchmarking process will compare advanced solvers against established classical baselines. Key metrics will include solution quality, computational runtime, and scalability to larger problem instances. The evaluation will also consider the practicality of deploying these algorithms in defense environments, where reliability, security, and speed of decision-making are critical. The project is led by Nathan Klose, Vice President of Davidson Labs, and William "whurley" Hurley, Founder and CEO of Strangeworks. Both organizations are active in Huntsville's growing quantum technology ecosystem, with Davidson serving as a founding member of the Southeastern Quantum Collaborative.
Context and Limitations
The collaboration is part of a broader trend in the defense sector to explore quantum and hybrid computing for optimization tasks that challenge classical methods. However, the current generation of quantum hardware remains limited by noise, error rates, and restricted qubit counts, making it uncertain whether quantum approaches can consistently outperform optimized classical algorithms for large-scale, real-world problems. The companies have not released peer-reviewed results or independent verification of performance gains. As with other recent defense-focused quantum initiatives, such as the addition of QuProtect R3 to federal procurement channels for post-quantum cryptography (federal agencies' cryptographic migration efforts), the practical impact of quantum technologies will depend on rigorous benchmarking and transparent reporting of both strengths and limitations.
Hybrid quantum-classical optimization refers to computational workflows that combine quantum algorithms-often run on noisy intermediate-scale quantum (NISQ) devices-with classical pre- and post-processing. In these systems, quantum processors may tackle subproblems or generate candidate solutions, while classical computers handle large-scale data management and solution refinement. The effectiveness of such hybrid approaches depends on the quality of quantum hardware, the suitability of the problem for quantum speedup, and the efficiency of integrating quantum and classical resources. Benchmarking against strong classical baselines is essential to determine whether quantum or hybrid methods offer genuine operational value beyond current best-in-class algorithms.
