Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems

Optimization has always been viewed as a central component of many electrical engineering techniques, where it involves designing a complex system with various constraints and competing objectives. The method described in this work proposes a hybrid quantum-classical evolutionary optimization algorithm targeting high-frequency electromagnetic problems. A genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity is introduced. This change means that stagnation can be reduced without compromising the speed of convergence. This was used on both real quantum hardware as well as quantum simulators. The results demonstrate that the performance of the real quantum devices was deteriorated by the noise in these devices and that simulators would be a useful option. We provide a description of the operation of the proposed evolutionary optimization method with mathematical benchmarks and electromagnetic design problems that show that it outperforms conventional evolutionary algorithms in terms of convergence behavior and robustness.