Abstract: Wireless network coverage and capacity optimization (NCO) is usually a multi-variate combinatorial optimization problem, and traditional precise or heuristic methods face time complexity or solution accuracy bottlenecks in solving it. Therefore, this article transforms NCO into the Maximum Independent Set (MIS) problem in graph theory, allocates wireless resources simultaneously to more users without interference, and uses Quantum Approximation Optimization Algorithm (QAOA) to solve it. Firstly, a mathematical model is constructed to encode the feasible solution of the MIS problem into the target Hamiltonian ground state. Then, a classical optimizer is used to optimize the QAOA parameterized quantum circuit to achieve the preparation of the target Hamiltonian ground state. Finally, algorithm simulation is conducted on the Huawei mindspore quantum platform and performance comparison is made with the Graph Neural Network (GNN). The simulation results show that QAOA can find the exact solution or the quasi-optimal solution of the MIS problem in O[poly(n)] time, demonstrating some certain quantum advantages.

Key words: Quantum Approximate Optimization Algorithm, Wireless Network Optimization, Maximum Independent Set