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Optimization parameter prediction-based XGBoost of TF-QKD

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Abstract

Twin-field quantum key distribution (TF-QKD) can overcome the basic limits of QKD without repeaters. In practice, TF-QKD needs to optimize all parameters when limited data sets are considered. The traditional exhaustive traversal or local search algorithm can’t meet the time and resource requirements of the real-time communication system. Combined with machine learning, parameter optimization prediction of QKD has become the mainstream of parameter optimization. Random forest (RF) is a classical algorithm of the bagging class in integrated learning, and back-propagation neural network (BPNN) is an important algorithm in the neural network. This paper uses the extreme gradient boosting (XGBoost) of boosting class to predict the optimization parameters of TF-QKD and compares it with RF and BPNN. The results show that XGBoost can efficiently and accurately predict optimization parameters, and its performance is slightly better than RF and BPNN in parameter prediction, which can provide a reference for future real-time QKD networks.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. 61571060), Ministry of Science and Technology of China (Grant No. 2016YFA0301300) and Fundamental Research Funds for the Central Universities (Grant No. 2019XD-A02).

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qin Dong, Guoqi Huang, Wei Cui & Rongzhen Jiao

  2. State Key Laboratory of Information Photonics and Optical Communication, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Rongzhen Jiao

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  1. Qin Dong
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  2. Guoqi Huang
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  3. Wei Cui
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  4. Rongzhen Jiao
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Correspondence to Rongzhen Jiao.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Dong, Q., Huang, G., Cui, W. et al. Optimization parameter prediction-based XGBoost of TF-QKD. Quantum Inf Process 21, 233 (2022). https://doi.org/10.1007/s11128-022-03579-6

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