量子密钥分发作为量子机器学习任务

IF 8.3 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Thomas Decker, Marcelin Gallezot, Sven Florian Kerstan, Alessio Paesano, Anke Ginter, Wadim Wormsbecher
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引用次数: 0

摘要

我们建议将量子密钥分发(QKD)协议作为量子机器学习(QML)算法的用例。我们定义并研究了在BB84协议的量子电路实现上优化窃听攻击的QML任务。QKD协议被很好地理解,并且存在可靠的安全性证明,可以轻松评估QML模型的性能。通过在无噪声环境中找到最优单个攻击的显式电路,显示了易于实现的QML技术的强大功能。对于有噪声的设置,我们发现,据我们所知,一个新的克隆算法,它可以优于已知的克隆方法。最后,我们利用QML算法中QKD后处理的经典信息,提出了一个QML构造集体攻击的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Quantum key distribution as a quantum machine learning task

Quantum key distribution as a quantum machine learning task

We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understood and solid security proofs exist enabling an easy evaluation of the QML model performance. The power of easy-to-implement QML techniques is shown by finding the explicit circuit for optimal individual attacks in a noise-free setting. For the noisy setting we find, to the best of our knowledge, a new cloning algorithm, which can outperform known cloning methods. Finally, we present a QML construction of a collective attack by using classical information from QKD post-processing within the QML algorithm.

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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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