Optimization of Quantum Key Distribution Parameters Based on Random Forest

For the actual quantum key distribution (QKD) system, parameter optimization, the selection of the corresponding signal strength and transmission probability are the keys to obtain the best performance, especially when performing low-latency communication. Combining the random forest algorithm with data preprocessing, according to the relative performance parameters of each decision tree, the decision trees with poor performance is eliminated, so that the random forest algorithm is improved. This method is used to predict the best parameters of the QKD system directly, instead of searching for the best parameters. We tested the random forest algorithms on hardware equipment. We find that, compared with the traditional random forest algorithm, we have made great progress and achieved a relatively high optimal key rate. It has broad application prospects in the field of QKD.