Parameters Optimization of Decoy-State Phase-Matching Quantum Key Distribution Based on the Nature-inspired Algorithms

Phase-Matching quantum key distribution (PM-QKD) has achieved significant results in practical applications; however, real-time communication necessitates the dynamic adjustment and optimization of key parameters during the communication process. In this paper, we predict the parameters of PM-QKD using Nature-inspired Algorithms (NIAs), with results obtained from an Exhaustive Traversal Algorithm (ETA) serving as the benchmark. We mainly study the parameter optimization effects of the two NIAs which are Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Meanwhile the configuration of the inherent parameters of these algorithms in decoy-state PM-QKD is discussed. The simulation results indicate that the parameters obtained by ACO exhibits superior convergence and stability, while the results of the GA are relatively scattered. Nevertheless, over 97% of the key rates predicted by both algorithms are highly consistent with the optimal key rate, with the relative error of key rates remaining below 10%. Furthermore, NIAs not only keep power consumption below 8W, but also require three orders of magnitude less computing time than ETA.