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.

翻译：量子密码学能够提供极高等级的数据安全性。然而，该技术面临的一大挑战在于量子信道中存在的错误。因此，在实际应用中必须采用纠错方法，例如基于人工神经网络的纠错方案。本文探讨了这种近期提出的方法在实际应用中的关键问题，并分析了影响安全性和效率的各要素。我们详细研究了基于相互学习过程的同步机制，相关结果使我们得以确定不同参数的影响。此外，本文还针对不同结构的人工神经网络及多种误码率，给出了推荐的迭代次数。所有这些旨在协助用户选择安全高效的人工神经网络配置以纠错。