The article discusses a key reconciliation protocol for quantum key distribution (QKD) systems based on Tree Parity Machines (TPM). The idea of transforming key material into neural network weights is presented. Two experiments were conducted to study how the number of synchronization iterations and the amount of leaked information depend on the quantum bit error rate (QBER) and the range of neural network weights. The results show a direct relationship between the average number of synchronization iterations and QBER, an increase in iterations when the weight range is expanded, and a reduction in leaked information as the weight range increases. Based on these results, conclusions are drawn regarding the applicability of the protocol and the prospects for further research on neural cryptographic methods in the context of key reconciliation.

翻译：本文探讨了一种基于树奇偶机（TPM）的量子密钥分发（QKD）系统密钥协调协议。提出了将密钥材料转换为神经网络权重的思路。通过两项实验，研究了同步迭代次数与信息泄露量如何依赖于量子比特错误率（QBER）及神经网络权重的取值范围。结果表明：平均同步迭代次数与QBER呈直接正相关关系；当权重范围扩大时迭代次数增加；随着权重范围增大，信息泄露量减少。基于这些结果，本文就该协议的适用性以及神经网络密码学方法在密钥协调领域的进一步研究前景得出了结论。