Comparative Study of Fast Optimization Method for Four-Intensity Measurement-Device-Independent Quantum Key Distribution Through Machine Learning

Abstract

The four-intensity protocol for measurement-device-independent (MDI) quantum key distribution (QKD) is renowned for its excellent performance and extensive experimental implementation. To enhance this protocol, a machine learning-driven rapid parameter optimization method is developed. This initial step involved a speed-up technique that quickly pinpoints the worst-case scenarios with minimal data points during the optimization phase. This is followed by a detailed scan in the key rate calculation phase, streamlining data collection to fit machine learning timelines effectively. Several machine learning models are assessed—Generalized Linear Models (GLM), k-Nearest Neighbors (KNN), Decision Trees (DT), Random Forests (RF), XGBoost (XGB), and Multilayer Perceptron (MLP)—with a focus on predictive accuracy, efficiency, and robustness. RF and MLP were particularly noteworthy for their superior accuracy and robustness, respectively. This optimized approach significantly speeds up computation, enabling complex calculations to be performed in microseconds on standard personal computers, while still achieving high key rates with limited data. Such advancements are crucial for deploying QKD under dynamic conditions, such as in fluctuating fiber-optic networks and satellite communications.