首页|Gray code based gradient-free optimization algorithm for parameterized quantum circuit

Gray code based gradient-free optimization algorithm for parameterized quantum circuit

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A Gray code based gradient-free optimization(GCO)algorithm is proposed to update the parameters of parameterized quantum circuits(PQCs)in this work.Each parameter of PQCs is encoded as a binary string,named as a gene,and a genetic-based method is adopted to select the offsprings.The individuals in the offspring are decoded in Gray code way to keep Hamming distance,and then are evaluated to obtain the best one with the lowest cost value in each iteration.The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached.The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets,and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm.The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks.Importantly,the GCO algorithm has a robust performance in the noise environment.

gradient-free optimizationGray codegenetic-based method

张安琪、武春辉、赵生妹

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Institute of Signal Processing and Transmission,Nanjing University of Posts and Telecommunications,Nanjing 210003,China

Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education),Nanjing University of Posts and Telecommunications,Nanjing 210003,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaPostgraduate Research & Practice Innovation Program of Jiangsu Province

6187123462375140KYCX19_0900

2024

10.1088/1674-1056/ad09cd

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(2)
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