Optimal protocol Key generating rates for 3 QKD-protocol under different scenarios

AbstractQuantum key distribution (QKD) provides us with an unprecedentedly secure way of communication, and the future of QKD protocols is therefore extremely promising. This dataset concerns the key generating rates of 3 well-known protocols under different scenarios. These protocols are BB84, Measurement device independent QKD (MDI-QKD) and Twin-Field QKD (TF-QKD). What compose a specific scenario are 5 features: dark count rate (Y0), misalignment error rate (ed), efficiency of single photon detectors (η), numbers of pulses (N), and transmission distance (L). It also features a pair of training and testing set which we use to train a machine learning model to determine which protocol is the optimal one for a specific scenario.

摘要：量子密钥分发（Quantum Key Distribution, QKD）为人类提供了一种前所未有的安全通信方式，量子密钥分发协议的未来发展前景极为广阔。本数据集聚焦于三种知名协议在不同场景下的密钥生成速率，这三种协议分别为BB84协议、测量设备无关量子密钥分发（Measurement Device Independent QKD, MDI-QKD）以及双场量子密钥分发（Twin-Field QKD, TF-QKD）。特定场景由五项特征构成：暗计数率（dark count rate, Y0）、失准误差率（misalignment error rate, ed）、单光子探测器效率（efficiency of single photon detectors, η）、脉冲数（numbers of pulses, N）以及传输距离（transmission distance, L）。本数据集还配套了训练集与测试集，用于训练机器学习模型以判定特定场景下的最优协议。