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An improved hybrid quantum optimization algorithm for solving nonlinear equations

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Abstract

This paper proposes a hybrid quantum genetic algorithm (HQGA) and the core is the use of a new quantum revolving gate strategy and population adaptive retention strategy, and with the Quasi-Newton method. HQGA has the characteristics of fast convergence speed, strong global optimization ability and local detailed optimization. Through the typical complex function, tests show that the optimized quality and efficiency of HQGA are better than traditional quantum genetic algorithms. This algorithm has a good effect in training logistic regression (convex problem) in machine learning.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61772295,61572270 and 61173056); the PHD foundation of Chongqing Normal University No.19XLB003; Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJZD-M202000501 ); Chongqing Technology Innovation and application development special general project(cstc2020jscx-lyjsA0063). Chongqing Normal University Graduate Scientific Research Innovation Project Grant YKC20042.

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  1. Chongqing Normal University, Chongqing, China

    Yumin Dong & Jinlei Zhang

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Dong, Y., Zhang, J. An improved hybrid quantum optimization algorithm for solving nonlinear equations. Quantum Inf Process 20, 134 (2021). https://doi.org/10.1007/s11128-021-03067-3

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