Large-scale wireless coverage optimization: A quantum approach

Abstract

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.