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Quantum Information Processing

Optimization parameter prediction-based XGBoost of TF-QKD

  • 01-07-2022
Published in:
Authors
Qin Dong
Guoqi Huang
Wei Cui
Rongzhen Jiao
Published in
Quantum Information Processing | Issue 7/2022

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Abstract

This article delves into the optimization of TF-QKD system parameters using machine learning algorithms, with a particular focus on XGBoost. It begins by introducing the theoretical basis of TF-QKD and XGBoost, followed by a detailed simulation of the effects of various system parameters on key rates and transmission distances. The study compares the performance of XGBoost with BPNN and RF in predicting optimization parameters, showcasing XGBoost's efficiency and accuracy. The research highlights the significant impact of system parameters such as channel optical error rate, dark count of detectors, total number of signals, and the number of parts of the phase on key rates. Moreover, it demonstrates the potential of XGBoost in reducing optimization time while maintaining high prediction accuracy, making it a valuable tool for future real-time quantum communication systems. The article concludes by emphasizing the importance of these findings for the establishment of QKD real-time network communication.
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previous article Toward multimode quantum Rabi model in a strong-coupling cavity optomechanical system
next article Micro–micro and micro–macro entanglement witnessing via the geometric phase in an impurity-doped Bose–Einstein condensate
Title
Optimization parameter prediction-based XGBoost of TF-QKD
Authors
Qin Dong
Guoqi Huang
Wei Cui
Rongzhen Jiao
Publication date
01-07-2022
Publisher
Springer US
Published in
Quantum Information Processing / Issue 7/2022
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-022-03579-6
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