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Cryptosystem for Grid Data Based on Quantum Convolutional Neural Networks and Quantum Chaotic Map

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Abstract

Motivated by the existing circuit model of quantum convolutional neural network, a new quantum convolutional neural network circuit model is devised, which is combined with quantum chaotic map to construct a symmetric cryptosystem. Quantum chaotic map produces key stream for encryption and decryption. The cryptosystem simulates the basic process of communication. Theoretical analysis manifests that the cryptosystem is effective. Additionally, simulation experiments based on MNIST data set show that the cryptosystem is secure. Furthermore, the proposed cryptosystem can be applied not only for image data, but for text data. Therefore, the grid data can be encrypted by utilizing the cryptosystem.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant no. 61871205).

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Authors and Affiliations

  1. Information & Telecommunication Branch of State Grid Jiangxi Electric Power Co., Ltd., Nanchang, 330096, China

    Ru-Chao Tan, Xing Liu, Ru-Gao Tan, Jian Li, Hui Xiao, Jian-Jun Xu, Ji-Hai Yang, Yang Zhou, De-Lin Fu & Fang Yin

  2. Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China

    Lang-Xin Huang & Li-Hua Gong

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  1. Ru-Chao Tan
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  2. Xing Liu
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  3. Ru-Gao Tan
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Correspondence to Li-Hua Gong.

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Tan, RC., Liu, X., Tan, RG. et al. Cryptosystem for Grid Data Based on Quantum Convolutional Neural Networks and Quantum Chaotic Map. Int J Theor Phys 60, 1090–1102 (2021). https://doi.org/10.1007/s10773-021-04733-z

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  • DOI: https://doi.org/10.1007/s10773-021-04733-z

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