Simulator of Quantum Bit Error Correction Process based on Artificial Neural Networks

Quantum key distribution is a promising technology for ensuring secure communication in the era of quantum computing. However, its practical implementation faces challenges related to error correction due to the imperfections of quantum channels. Recently, artificial neural networks have shown significant potential for enhancing this process. This paper introduces a simulation tool for analyzing the security and efficiency of artificial neural networks in correcting errors within quantum key distribution systems. The tool explores various configurations, error levels, and synchronization dynamics, providing insights into the iteration requirements for legitimate parties compared to an attacker attempting to interfere. The simulator’s flexibility allows for the implementation of alternative learning algorithms and input types, offering a foundation for advancing neural network-based error correction in quantum cryptography. Additionally, the study highlights the impact of quantum bit error rates and error types on correction performance.