Implementation of Machine Learning in Quantum Key Distributions

In the future massive applications of quantum communication network, it is crucial to realize real-time selection of the optimal quantum key distributions (QKD) protocol for the improvement of system security and optimizing resources configuration. In principle, this can be done by utilizing algorithms such as exhaustive traversal or local search algorithm, whose time cost, however, is unbearable. Here we for the first time propose to employ machine learning methods into the selecting of optimal QKD protocol and apply random forest (RF) as an example for illustration. With the help of the easy-to-train classifier of RF, we can achieve a highly efficient optimal QKD protocols selector. Besides, we also do comparisons among RF and other machine learning methods, e.g., supporting vector machine, K-nearest neighbors algorithm, multinomial naive bayes classifier, and convolutional neural networks. Results demonstrate that, besides its advantage in efficiency, the RF classifier also excels both in preciseness and robustness with an accuracy over 98% for the testing set and an enjoyable receiver operating characteristic.