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量子测量的统计干扰界限

The statistical disturbance bound of quantum measurements

Ties-A. Ohst, Sebastian Schlösser, Roope Uola

arXiv 2607.15874首次发表:更新:

AI 中文总结

研究量子测量的统计干扰界限,引入相关界限连接测量统计特性与状态干扰。开发加权态排除技术,该技术能实验确定界限,相比现有关系有优势,还能用于限制量子随机数生成协议中窃听者猜测概率。

AI 中文摘要

量化量子测量引起的干扰通常需要对基础测量通道有详细了解。在这项工作中,我们引入了一个统计干扰界限,它将量子测量的统计特性与任何兼容测量通道引起的状态干扰联系起来。具体而言,我们表明,对于任意纯输入态系综,输入与输出之间的平均保真度在测量方面从根本上是有界的,测量被描述为一个正算子值测量(POVM)。我们进一步开发了加权态排除技术,它能够在无需明确测量效应知识的情况下通过实验确定统计干扰界限。为了看出我们的方法相对于现有信息干扰关系的优势,我们表明我们的界限能够区分具有等效信息量的测量。此外,我们证明加权态排除技术可以使用对于测量算子的断层扫描重建不足的态制备来检测和量化测量引起的干扰。最后,我们说明了如何使用针对特定输入系综定义的干扰界限来限制量子随机数生成简单协议中窃听者的猜测概率。

英文摘要

Quantifying the disturbance caused by a quantum measurement typically requires detailed knowledge of the underlying measurement channel. In this work, we introduce a statistical disturbance bound, which connects the statistical properties of a quantum measurement to the state disturbance induced by any compatible measurement channel. Specifically, we show that the average fidelity between input and output with respect to an arbitrary ensemble of pure input states is fundamentally bounded in terms of the measurement, described as a positive operator-valued measure (POVM). We further develop the weighted state exclusion technique, which enables an experimental determination of the statistical disturbance bound without requiring explicit knowledge of the measurement effects. To see the advantages of our approach over existing information-disturbance relations, we show that our bound distinguishes between measurements with equivalent informativeness. Furthermore, we demonstrate that the weighted state exclusion technique can detect and quantify measurement-induced disturbance using state preparations that are insufficient for tomographic reconstruction of the measurement operators. Finally, we illustrate how disturbance bounds defined with respect to specific input ensembles can be used to bound an eavesdropper's guessing probability in a simple protocol for quantum randomness generation.

Comments16 pages, 6 figures, comments are welcome!

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