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

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.

Automated summary

Inspired by the existing circuit model of quantum convolutional neural network, a new quantum convolutional neural network circuit model is proposed, incorporating quantum chaotic map to establish a symmetric cryptosystem. Theoretical analysis and simulation experiments with MNIST dataset demonstrate the effectiveness and security of the cryptosystem, which can be utilized for encrypting both image and text data.