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arXiv 2409.03360quant-ph
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通过QBER时间序列分析实现QKD链路的实时诊断

Real-time diagnostics on a QKD link via QBER Time Series Analysis

G. Maragkopoulos, A. Mandilara, T. Nikas, D. Syvridis

AI总结:

本文提出一种基于QBER和SKR时间序列的监督式机器学习方法,用于实时识别城域光网络中QKD量子信道所受的各类损伤,无需干预QKD系统,为网络运维和密钥管理提供支持。

摘要:

将QKD系统集成到城域光网络中带来了当前技术水平无法完全解决的挑战。在这项工作中,我们设计了一种方法,用于识别量子信道在运营网络中传输期间可能出现的不同类型的损伤。该方法围绕一个监督式机器学习流水线构建,该流水线使用QBER和SKR时间序列作为输入,并且不需要对QKD系统进行进一步干预。损伤的识别是实时进行的,尽管这些信息无法逆转已发生的事件,但对用户、运营商和密钥管理系统而言可能具有重要价值。

英文摘要:

The integration of QKD systems in Metro optical networks raises challenges which cannot be completely resolved with the current technological status. In this work we devise a methodology for identifying different kind of impairments which may occur on the quantum channel during its transmission in an operational network. The methodology is built around a supervised ML pipeline which is using as input QBER and SKR time-series and requires no further interventions on the QKD system. The identification of impairments happens in real time and even though such information cannot reverse incidents, this can be valuable for users, operators and key management system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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