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<p>In the application of quantum key distribution, besides security being a necessary consideration, real-time performance is also very critical. Exhaustive traversal or local search algorithm has been commonly utilized in previous applications; however, these algorithms cannot satisfy the low latency requirement well. Therefore, in order to achieve efficient quantum key distribution parameter optimization and selection, the particle swarm optimization algorithm applied to twin-field quantum key distribution is proposed and compared with the results of the exhaustive traversal algorithm in this paper. The results show that particle swarm optimization algorithm can optimize the parameters to 15 decimal places; the algorithm performs well in terms of accuracy and robustness, and the parameter optimization accuracy is more than 99.5%. Meanwhile, the minimum running time is 5.68&#xa0;s when guaranteeing the above values in terms of the accuracy of the optimization results.</p>

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Twin-field quantum key distribution based on particle swarm optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年2月17日

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  • Chang Liu,
  • Yue Li,
  • Haoyang Wang,
  • Kaiyi Shi,
  • ZhongQi Sun,
  • Jiaao Li,
  • Yujia Zhang,
  • Duo Ma,
  • Haiqiang Ma

摘要

In the application of quantum key distribution, besides security being a necessary consideration, real-time performance is also very critical. Exhaustive traversal or local search algorithm has been commonly utilized in previous applications; however, these algorithms cannot satisfy the low latency requirement well. Therefore, in order to achieve efficient quantum key distribution parameter optimization and selection, the particle swarm optimization algorithm applied to twin-field quantum key distribution is proposed and compared with the results of the exhaustive traversal algorithm in this paper. The results show that particle swarm optimization algorithm can optimize the parameters to 15 decimal places; the algorithm performs well in terms of accuracy and robustness, and the parameter optimization accuracy is more than 99.5%. Meanwhile, the minimum running time is 5.68 s when guaranteeing the above values in terms of the accuracy of the optimization results.