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

Abstract Quantum 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.

量子密钥分发（QKD）为我们提供了一种前所未有的通信安全性保障，因此，QKD 协议的未来发展前景极为广阔。本数据集聚焦于在多种场景下，三种知名协议（BB84、测量设备无关量子密钥分发（MDI-QKD）和双场量子密钥分发（TF-QKD））的密钥生成速率。特定场景由五个特征构成：暗计数率（Y0）、失调误差率（ed）、单光子探测器效率（η）、脉冲数量（N）和传输距离（L）。此外，数据集还包括一组训练集和测试集，我们利用这些数据集来训练机器学习模型，以确定针对特定场景的最佳协议。