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Quantum approximate optimization algorithm for the tactical clustering and grouping problems
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DOI：10.7498/aps.75.20251690.png)
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Abstract
En 中文
To address the challenge of complex multi-resource constraints in platform grouping for tactical operations, this study develops a quantum-enhanced solution optimization framework using the quantum approximate optimization algorithm (QAOA). By decomposing the problem into sequential phases of resource matching and cluster optimization, and leveraging a hybrid quantum-classical approach, the framework is designed to efficiently generate optimal platform grouping schemes. As shown in figure, first, the problem was decomposed into two interrelated subproblems: resource matching and platform assignment. A quantum Ising model was formulated for the integer knapsack problem, and a QAOA quantum circuit was designed. Parameter optimization was then performed to generate candidate platform clusters that satisfy task cluster resource requirements; second, leveraging the exact set cover problem as a framework, a corresponding quantum model was formulated and optimally solved using hybrid quantum-classical optimization. This process identified the globally optimal clustering scheme that ensures both platform uniqueness and complete set coverage; finally, an efficient solution for platform clustering under complex constraints was developed by reformulating the classical problem into a quantum quantum Ising model and integrating a parameterized quantum circuit with classical optimizers through hybrid quantum-classical optimization. The experiments were conducted in a Python 3-based quantum software development environment and quantum computing cloud service platform. The experimental results demonstrate that the proposed quantum-enhanced optimization framework significantly outperforms traditional algorithms in platform allocation efficiency, with the time complexity reduced from O(n(2)) to O(5n + 5k) Compared to conventional multi-dimensional dynamic list programming and multi-priority list dynamic programming methods, illustrating a distinct advantage. The study confirms that the QAOA-based framework can effectively address complex platform clustering and grouping problems in tactical operations, thereby laying a foundation for the application of quantum computing in command-and-control and resource optimization domains.
Keywords:
tactical operations
platform clustering and grouping
quantum approximate optimization algorithm
quantum ising model
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