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

Authors :
Li Jian
De-Lin Fu
Tan Ruchao
Yang Jihai
Yin Fang
Xiao Hui
Li-Hua Gong
Liu Xing
Zhou Yang
Xu Jianjun
Ru-Gao Tan
Lang-Xin Huang
Source :
International Journal of Theoretical Physics. 60:1090-1102
Publication Year :
2021
Publisher :
Springer Science and Business Media LLC, 2021.

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.

Details

ISSN :
15729575 and 00207748
Volume :
60
Database :
OpenAIRE
Journal :
International Journal of Theoretical Physics
Accession number :
edsair.doi...........87020d57e1b074358e209944867e310e
Full Text :
https://doi.org/10.1007/s10773-021-04733-z