Hybrid Variational Quantum Algorithm Enhanced Subentire-Domain Basis Functions Method With High Learning Efficiency and Better Robustness

The subentire-domain (SED) basis functions method is the most effective method for analyzing the electromagnetic properties of large-scale finite periodic structures (LFPSs). Recently, artificial neural networks have been employed to accelerate this method by rapidly predicting the expansion coefficients of SED basis functions without filling mutual coupling matrices. However, the training processes of prediction models can be further improved due to its classical computational paradigm. In this paper, a novel variational quantum algorithm enhanced SED basis functions method is proposed and the quantum computing paradigm is utilized to analyze LFPSs for the first time. In our algorithm, the array features are expanded and encoded onto few qubits, and the resulting quantum state is unitarily transformed into expansion coefficients by the parameterized quantum circuit. In addition, the algorithm is deployed on the quantum simulator for numerical experiments. The experimental results demonstrate that our method can accurately and quickly analyze LFPSs. Furthermore, the quantum-inspired models achieve 27%-62% improvements in learning efficiency for corner and edge cells, and 22%-59% improvements in robustness for all types of cells.