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Parameter optimization for mode-pairing quantum key distribution based on LightGBM
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DOI：10.1364/OPTCON.581743.png)
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Abstract
En 中文
The mode-pairing quantum key distribution (MP-QKD) achieves high key generation rates and high security over long-distance, low-loss optical fibers through its mode-pairing technique. In practical applications, when considering finite data sets, MP-QKD requires optimization of parameters such as channel optical bit error rate (eopt), detector's dark count(Pd), the total number of signals(N), and the number of phase slices(M). However, traditional exhaustive traversal or local search algorithms do not meet the time and resource requirements of real-time communication systems. Combined with machine learning, the quantum key distribution for parameter optimization prediction has become the mainstream approach. XGBoost, a gradient boosting decision tree (GBDT) algorithm, random forest (RF) representing a classic bagging-based ensemble learning method, and back-propagation neural networks (BPNN) are important algorithms in neural networks. This study employs the LightGBM (light gradient boosting machine) algorithm to predict optimized parameters for MP-QKD. The results demonstrate that LightGBM efficiently and accurately predicts optimal parameters, slightly outperforming XGBoost, RF, and BPNN in parameter prediction, and can serve as a reference for future real-time QKD networks. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keywords:
LightGBM
mode-pairing quantum key distribution
parameter optimization
machine learning
quantum key distribution
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O
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1.4
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