利用顺序量子测量实现安全稳健的随机性

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Matteo Padovan, Giulio Foletto, Lorenzo Coccia, Marco Avesani, Paolo Villoresi, Giuseppe Vallone
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引用次数: 0

摘要

在随机性生成和密钥分配等应用中,不同观察者测量值之间的量子相关性至关重要。虽然独立于设备的安全性可以通过最少的假设得到认证,但目前的协议性能有限。在这里,我们利用以精确时间顺序定义的顺序测量,通过重复使用量子态来提高性能。我们提供了一个几何视角和通用数学框架,分析证明了顺序量子相关性的齐列尔松边界,它代表了顺序用户共享的非位置性的权衡。这一边界有利于安全量子随机性的生成,即使一个顺序用户不共享非位置性,也能通过一个远程和两个顺序方认证每个状态的最大比特数。我们的简单量子比特协议达到了这一界限,数值分析表明在现实噪声下的鲁棒性有所提高。光子实现证实了其可行性和稳健性。这项研究加深了人们对顺序量子相关性的理解,并为独立于设备的高效协议提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Secure and robust randomness with sequential quantum measurements

Secure and robust randomness with sequential quantum measurements

Quantum correlations between measurements of separated observers are crucial for applications like randomness generation and key distribution. Although device-independent security can be certified with minimal assumptions, current protocols have limited performance. Here, we exploit sequential measurements, defined with a precise temporal order, to enhance performance by reusing quantum states. We provide a geometric perspective and a general mathematical framework, analytically proving a Tsirelson-like boundary for sequential quantum correlations, which represents a trade-off in nonlocality shared by sequential users. This boundary is advantageous for secure quantum randomness generation, certifying maximum bits per state with one remote and two sequential parties, even if one sequential user shares no nonlocality. Our simple qubit protocol reaches this boundary, and numerical analysis shows improved robustness under realistic noise. A photonic implementation confirms feasibility and robustness. This study advances the understanding of sequential quantum correlations and offers insights for efficient device-independent protocols.

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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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