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Can quantum genetic algorithm really improve quantum backpropagation neural network?

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Abstract

The key point of introducing quantum genetic algorithm to a quantum backpropagation neural network model is to overcome local stagnation problem which used to be Achilles’ heel. In this paper, we propose a new quantum backpropagation (QBP) model based on the quantum genetic algorithm (QGA) and make simulations with this model to see whether QGA can really upgrade QBP and, in addition, to ensure that both quantum neural networks are better than classical backpropagation (CBP) neural networks from many points of view. Numerical experiments have been built to illustrate the efficiency of the new QBP algorithm over CBP and the original QBP algorithm. However, the proposed model has shown superior results to the rest of models in terms of correction rate and training time. That is to say quantum genetic algorithm-based quantum backpropagation neural network converges earlier than the other two models and that’s why we can reduce the time needed to train.

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All the references cited are available. All data and material considered in this paper are transparent.

Code availability

The authors have used MATLAB as software appliance and a Core i7-4720 laptop as hardware support.

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Acknowledgements

This work was supported by the National Program on Key Project for Frontier Research on Quantum Information and Quantum Optics of Democratic People’s Republic of Korea.

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Authors and Affiliations

  1. Faculty of Physics, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

    Il-Hyang Choe, Gwang-Jin Kim, Nam-Chol Kim, Myong-Chol Ko, Ju-Song Ryom, Ryong-Min Han & Tae-Gyong Han

  2. Institute of Information Technology, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

    Il Han

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  1. Il-Hyang Choe
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  7. Tae-Gyong Han
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Contributions

N-CK is the project manager. He designed the model and checked the accuracy of calculations. I-HC performed theoretical calculations and numerical calculations. She also analyzed the results and contributed to preparation of the manuscript. M-CK and J-SR checked the correctness of the theoretical calculations and the analysis. R-MH and T-GH checked the results of numerical calculations, and IH checked the validity of the set of parameters.

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Correspondence to Nam-Chol Kim.

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Choe, IH., Kim, GJ., Kim, NC. et al. Can quantum genetic algorithm really improve quantum backpropagation neural network?. Quantum Inf Process 22, 154 (2023). https://doi.org/10.1007/s11128-023-03858-w

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