Optimization parameter prediction-based XGBoost of TF-QKD

Twin-field quantum key distribution (TF-QKD) can overcome the basic limits of QKD without repeaters. In practice, TF-QKD needs to optimize all parameters when limited data sets are considered. The traditional exhaustive traversal or local search algorithm can’t meet the time and resource requirements of the real-time communication system. Combined with machine learning, parameter optimization prediction of QKD has become the mainstream of parameter optimization. Random forest (RF) is a classical algorithm of the bagging class in integrated learning, and back-propagation neural network (BPNN) is an important algorithm in the neural network. This paper uses the extreme gradient boosting (XGBoost) of boosting class to predict the optimization parameters of TF-QKD and compares it with RF and BPNN. The results show that XGBoost can efficiently and accurately predict optimization parameters, and its performance is slightly better than RF and BPNN in parameter prediction, which can provide a reference for future real-time QKD networks.