Real-time optimal protocol prediction of quantum key distribution using machine learning

• Published in: International journal of pervasive computing and communications Vol. 19; no. 5; pp. 689 - 697
• Main Authors: Arthi, R, Nayana, J S, Mondal, Rajarshee
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 16.11.2023
• Subjects: Accuracy; Algorithms; Artificial neural networks; Classification; Commercialization; Communication; Datasets; Decision trees; Efficiency; Machine learning; Neural networks; Protocol; Quantum cryptography; Search algorithms; Selectors; Sensors; Support vector machines; System effectiveness
• ISSN: 1742-7371; 1742-738X
• DOI: 10.1108/IJPCC-05-2022-0200

PurposeThe purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD.Design/methodology/approachThe proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN).FindingsBecause of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection.Originality/valueThe proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.