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：量子加密可以提供非常高的数据安全水平。 但是,这种技术的一大挑战就是量子信道中的错误。 因此, 错误纠正方法必须应用在实际执行中。 一个例子就是人工神经网络上的错误纠正。 本文件考虑了这一最近提出的方法的实际方面,并分析了影响安全和效率的因素。 基于相互学习过程的同步过程得到了详细分析。 其结果使我们能够确定各种参数的影响。 此外, 该文件描述了人工神经网络不同结构的推荐迭代数和各种错误率。 所有这些都旨在支持用户选择一种合适的神经网络配置, 用来以安全和高效的方式纠正错误。