Quantum Rotation Gate-Based Particle Swarm Algorithm for Test Data Anomaly Detection Model Hyperparameter Optimization

Particle Swarm Optimization (PSO) is a popular algorithm used for optimization problems in various domains. However, the PSO algorithm suffers from some limitations, e.g., premature convergence and slow convergence rate. In this paper, we propose a new algorithm called Quantum Rotation Gate Particle Swarm Optimization (QPSO), which is based on the quantum rotation gate to address these shortcomings. The proposed QPSO algorithm aims to improve the optimization capability of the original PSO. We applied QPSO to optimize the neural network architecture for numerical anomaly detection on the self-noise aerodynamic wind tunnel test data set. Good results are achieved and the effectiveness of the proposed method is demonstrated. Compared to the original PSO algorithm, QPSO outperforms it in terms of convergence rate and accuracy.