Secure synchronization of artificial neural networks used to correct errors in quantum cryptography

TL;DR

This paper analyzes the practical aspects of using neural networks for error correction in quantum cryptography, focusing on synchronization, security, and efficiency to guide optimal neural network configurations.

Contribution

It provides a detailed analysis of synchronization processes and parameter impacts, offering practical recommendations for neural network configurations in quantum error correction.

Findings

Optimal number of iterations varies with network structure and error rate

Synchronization process impacts security and efficiency

Guidelines for selecting neural network parameters in quantum error correction

Abstract

Quantum cryptography can provide a very high level of data security. However, a big challenge of this technique is errors in quantum channels. Therefore, error correction methods must be applied in real implementations. An example is error correction based on artificial neural networks. This paper considers the practical aspects of this recently proposed method and analyzes elements which influence security and efficiency. The synchronization process based on mutual learning processes is analyzed in detail. The results allowed us to determine the impact of various parameters. Additionally, the paper describes the recommended number of iterations for different structures of artificial neural networks and various error rates. All this aims to support users in choosing a suitable configuration of neural networks used to correct errors in a secure and efficient way.