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

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 exi...

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Bibliographic Details
Published in:The Journal of supercomputing 2023-08, Vol.79 (12), p.13421-13441
Main Authors: Gong, Changqing, Zhu, Hongsheng, Gani, Abdullah, Qi, Han
Format: Article
Language:English
Subjects:
Accuracy
Compilers
Computer Science
Convergence
False alarms
Gates (circuits)
Genetic algorithms
Global optimization
Interpreters
Intrusion
Neural networks
Processor Architectures
Programming Languages
Quantum computing
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05158-7