Journal Article

QGA–QGCNN: a model of quantum gate circuit neural network optimized by quantum genetic algorithm

Qi, Han, Gong, Changqing, Gani, Abdullah, Zhu, Hongsheng

2023

Overview

Using a global optimization algorithm to optimize the initial weights and thresholds of traditional neural network models can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slow convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA–QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA–QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA–QGCNN model. Experimental results show that the proposed intrusion detection model has a lower false alarm rate and significant accuracy compared to conventional attack detection models. And QGCNN optimized by QGA improves the convergence performance of the model.

• Publisher: Springer US,Springer Nature B.V
• Subject: Accuracy / Artificial intelligence / Compilers / Computer Science / Convergence / False alarms / Gates (circuits) / Genes / Genetic algorithms / Global optimization / Interpreters / Neural networks / Optimization algorithms / Processor Architectures / Programming Languages / Quantum computing / Simulation