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Quantum Information Processing

Machine learning with neural networks for parameter optimization in twin-field quantum key distribution

Jia-Le Kang, Minghui Zhang, Xiaopeng Liu, Jiahui Xie

Published Aug 16, 2023• 8 citations
Publisher
DOI
10.1007/s11128-023-04063-5

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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 ( e opt ), detector’s dark count( P d ), 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.
Abstract In the continuous-variable quantum key distribution (CV-QKD) system, the secure key rate and the throughput of secure keys are important factors in measuring system quality. However, the secure key rate and the throughput of secure keys depend on various nonlinearly related parameters, which makes it difficult to effectively find the optimal parameters. In this paper, we introduce machine learning into the CV-QKD system and propose a parameter optimization scheme for the CV-QKD system based on the back-propagation (BP) neural network. The proposed scheme improves the secure key rate and throughput of secure keys by optimizing the code rate and the number of decoding iterations in the error correction process. Furthermore, under some reasonable assumptions, we prove that the optimization objective is a concave function of the optimization parameters under the BP neural network, which ensures that there exists a global optimal solution in a certain domain and provides a theoretical basis for the optimization based on BP neural networks. Simulation results show that compared with traditional optimization schemes, the proposed scheme can improve the secure key rate and transmission distance.