An Approach to Cryptography Based on Continuous-Variable Quantum Neural Network

Jinjing Shi,Shuhui Chen,Yanyan Feng,Ronghua Shi,Jian Li,Yuguang Yang,Yuhu Lu

doi:10.1038/s41598-020-58928-1

Abstract

An efficient cryptography scheme is proposed based on continuous-variable quantum neural network (CV-QNN), in which a specified CV-QNN model is introduced for designing the quantum cryptography algorithm. It indicates an approach to design a quantum neural cryptosystem which contains the processes of key generation, encryption and decryption. Security analysis demonstrates that our scheme is security. Several simulation experiments are performed on the Strawberry Fields platform for processing the classical data “Quantum Cryptography” with CV-QNN to describe the feasibility of our method. Three sets of representative experiments are presented and the second experimental results confirm that our scheme can correctly and effectively encrypt and decrypt data with the optimal learning rate 8e − 2 regardless of classical or quantum data, and better performance can be achieved with the method of learning rate adaption (where increase factor R1 = 2, decrease factor R2 = 0.8). Indeed, the scheme with learning rate adaption can shorten the encryption and decryption time according to the simulation results presented in Figure 12. It can be considered as a valid quantum cryptography scheme and has a potential application on quantum devices.

Highlights

Cryptography is one of the most crucial aspects for cybersecurity and it is becoming increasingly indispensable in information age
Continuous-variable quantum neural network (CV-QNN) model is utilized in this paper to design a more practical quantum cryptography scheme, which can be considered as an approach to quantum neural cryptography (QNC)
The capability of legitimate measurement bases (LMB) is introduced in the pre-process, which can solve the problem of cipher eavesdropping during the process of communications, though it may increase overheads

Summary

Introduction

Cryptography is one of the most crucial aspects for cybersecurity and it is becoming increasingly indispensable in information age. Sayantica[16] demonstrated hackers who have computational power polynomial in time cannot be able to invade in the neural network cryptosystem It provides an opportunity for the combination of quantum computing and neural cryptography[17]. Quantum neural network[18] was firstly proposed by Kak and it provided a potential solution to design novel encryption and decryption mechanism with computational efficiency, quantum natural properties, unidirectionality and multivariate structure of ANN. Continuous-variable quantum neural network (CV-QNN) model is utilized in this paper to design a more practical quantum cryptography scheme, which can be considered as an approach to quantum neural cryptography (QNC). The experimental results simulated on Strawberry Fields[32] demonstrate that the scheme can correctly encrypt and decrypt data and the method of learning rate adaption in our paper can accelerate the cryptography algorithm and strengthen the security of the cryptosystem

Methods

Results

Conclusion