Quantum-Driven UAV Path Planning Using QUBO Model and Coherent Ising Machines

UAV path planning in multi-target mission scenarios presents complex optimization challenges, particularly when considering constraints such as energy consumption and time efficiency. This paper proposes a quantum computing-driven approach using the QUBO model, integrated with a coherent Ising machine (CIM) on an optical quantum platform, to optimize UAV path planning. By modeling the path planning problem as a QUBO formulation and utilizing quantum heuristic algorithms for optimization, the proposed method overcomes the bottlenecks of traditional heuristic algorithms in terms of computational efficiency and convergence speed. Experimental results demonstrate that the proposed approach significantly outperforms traditional algorithms such as Tabu Search and Simulated Annealing, particularly as the number of target points increases. This method provides a new approach for applying quantum computing to UAV path planning, promoting the practical use of quantum technology in industrial applications.