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Large-scale wireless coverage optimization: A quantum approach
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DOI：10.1016/j.icte.2025.06.019.png)
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Abstract
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Wireless network coverage optimization is critical for improving service quality. However, optimizing large-scale networks remains challenging for both classical algorithms and quantum methods in the NISQ era. This paper proposes a quantum approach that models the problem as a covering graph, partitions it using a QUBO formulation, and solves subproblems via a filtered variational quantum eigensolver. The method is experimentally validated on real quantum hardware, including a coherent Ising machine and a superconducting quantum processor, and compared with classical methods like SA and PSO. This work introduces a divide-and-conquer strategy for large-scale network coverage optimization and expands the solution landscape.
Keywords:
Wireless coverage optimization
Quantum computing
Graph partitioning
QUBO
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