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The Journal of Supercomputing

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

Changqing Gong, Hongsheng Zhu, Abdullah Gani, Han Qi

Published Mar 24, 2023• 6 citations
Publisher

Abstract

Abstract Using global optimization algorithm to optimize the initial weights and thresholds of traditional neural network model can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slower 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. In the proposed model, the normalized results of the predicted sample attributes are transformed into quantum states as the input of the network, and the quantum rotation gate is used to rotate the phase and control the reversal of the target qubits. Finally, the output of the network is obtained by entanglement of multiple qubits in the quantum Controlled-NOT (CNOT) gate. 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 lower false alarm rate and significantly accuracy compared to conventional attack detection models. And GQCNN optimized by QGA also improves the convergence performance of the model.

DOI
10.1007/s11227-023-05158-7

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Abstract Using global optimization algorithm to optimize the initial weights and thresholds of traditional neural network model can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slower 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. In the proposed model, the normalized results of the predicted sample attributes are transformed into quantum states as the input of the network, and the quantum rotation gate is used to rotate the phase and control the reversal of the target qubits. Finally, the output of the network is obtained by entanglement of multiple qubits in the quantum Controlled-NOT (CNOT) gate. 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 lower false alarm rate and significantly accuracy compared to conventional attack detection models. And GQCNN optimized by QGA also improves the convergence performance of the model.
The current status of Quantum Neural Network (QNN) research is analyzed, it deeply studies the model Quantum Gate Circuit Neural Network (QGCNN) and QGCNN Learning Algorithm (QGCA). Replacing the mean square error function by using the Widrow function and the Rumelhart function, it creates two quantum neural network learning algorithms based on performance function, including Learning Algorithm of QGCNN based on the Widrow functions (WQGCA) and Learning Algorithm of QGCNN based on the Rumelhart functions (RQGCA). New algorithms overcome the inherent defects of the training result is not ideal and unrealistic, which is because the use of mean square error function training network will appear excessive punishment phenomenon. Three algorithms are trained by three data sets. Simulation Experiments on three kinds of algorithms was carried out by using these data sets in the case of the best learning rate. Research prove WQGCA and RQGCA have better pattern classification ability relative to QGCA and RQGCA has a higher classification accuracy than WQGCA.
Based on quantum genetic algorithm(QGA),a novel improved quantum genetic algorithm(NIQGA)to solve combinatorial optimization problem is proposed.To make full use of interference and entanglement characteristics of quantum state,dynamic step length in adjustment of angle of quantum gate,quantum crossover operation and quantum mutation operation are introduced,therefore high efficiency for optimization is achieved.Two typical combinatorial optimization problems—0/1 knapsack problem and route selection problem,are adopted to confirm the performance of NIQGA.Experimental results show that compared with GA and QGA,NIQGA is characterized by fast convergence rate and excellent capability on global optimization,especially better performance for combinatorial optimization problem with less correlation of genes.
Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.
We propose a gate-based Quantum Genetic Algorithm (QGA) for real-valued global optimization. In this model, individuals are represented by quantum circuits whose measurement outcomes are decoded into real-valued vectors through binary discretization. Evolutionary operators act directly on circuit structures, allowing mutation and crossover to explore the space of gate-based encodings. Both fixed-depth and variable-depth variants are introduced, enabling either uniform circuit complexity or adaptive structural evolution. Fitness is evaluated through quantum sampling, using the mean decoded output of measurement outcomes as the argument of the objective function. To isolate the impact of quantum resources, we compare gate sets with and without the Hadamard gate, showing that superposition consistently improves convergence and robustness across benchmark functions such as the Rastrigin function. Furthermore, we demonstrate that introducing pairwise inter-individual entanglement in the population accelerates early convergence, revealing that quantum correlations among individuals provide an additional optimization advantage. Together, these results show that both superposition and entanglement enhance the search dynamics of evolutionary quantum algorithms, establishing gate-based QGAs as a promising framework for quantum-enhanced global optimization.
It was indicated from the recent research that Quantum Genetic Algorithm(QGA) outperformed conventional genetic algorithm in its lower running time and ability of finding better solution when it solved a class of numerical and knapsack of combinatorial optimization problems.However,with qutbit of quantum individual interfering,parameter γ of the termination criterion is difficult to determine.The rotate gate which can promote the population is difficult to change its state of qutbit,and this phenomenon often induces premature.To improve the performance of the QGA,a controllable rotate gate and a new termination criterion are proposed in this paper.The controllable rotation gate can induce the convergence of qutbit not only to either 0 or 1,but also to either e or 1-e,and make the proposed algorithm to escape the local optimum.Based on gathering factor and qutbit convergence factor,a new termination criterion is proposed.The qutbit decreases the impact on parameter γ,and the new terminate criterion can efficiently control the relation of better solution and its running time.In addition,the Simplex method is introduced as a local searching scheme,which greatly improves the performance of algorithm.At last,the global convergence of the proposed algorithm is proved in theory,and comparable numerical experiments show that the proposed algorithm is more powerful than the compared algorithm in solution quality and speed.
Quantum Gate Circuit Neural Network(QGCNN)is a kind of quantum neural network model, which directly uses quantum theory to design the neural network topology or training algorithms. In the neural network, Momentum update is adding momentum parameter in weight renew and provides a specific inertia while renewing weight vector. It avoids sustained oscillation of weight vector in network training. It introduces the principle of momentum update in the basic learning algorithm of Quantum Gate Circuit Neural Network, proposes Quantum Gate Circuit neural network Momentum update Algorithm(QGCMA). The research shows that QGCMA has 100% convergence rate and enhances convergence speed compared to the basic algorithm with the same learning rate.