A machine learning and particle swarm optimization-based method for dynamic parameter optimization in mode-pairing quantum key distribution

Abstract Quantum key distribution (QKD) is a pivotal quantum communication technology but faces practical challenges such as transmission loss and measurement device vulnerabilities. Mode-pairing QKD (MP-QKD) addresses these limitations by dynamically pairing single-photon detection events, enabling secure long-distance communication with simplified experimental requirements. However, optimizing key parameters like pulse intensity remains computationally demanding for traditional numerical methods. To overcome this, this paper shows a machine learning framework that combines Particle swarm optimization (PSO) and three neural networks—Backpropagation neural network (BPNN), XGBoost, and Long short-term memory (LSTM)—to predict optimal pulse intensity under varying environmental conditions. Using PSO-generated datasets, the models map channel loss, error rates, transmission distance, and other parameters to pulse intensity. Simulations demonstrate that BPNN achieves 99.87% prediction accuracy (RMSE: 0.0026) with an average runtime of 13 milliseconds per prediction, outperforming XGBoost and LSTM in both speed and precision. This work establishes a scalable solution for real-time parameter optimization in large-scale QKD networks and highlights the transformative role of machine learning in advancing quantum communication technologies.