用量子统计查询学习量子过程

IF 5.1 2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY
Quantum Pub Date : 2025-05-12 DOI:10.22331/q-2025-05-12-1739
Chirag Wadhwa, Mina Doosti
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引用次数: 0

摘要

在这项工作中,我们启动了从量子统计查询中学习量子过程的研究。在这个新的访问模型中,我们关注两个基本的学习任务:量子过程的阴影层析成像和关于钻石距离的过程层析成像。对于前者,我们提出了一种有效的平均情况算法,以及关于待预测的可观测值数量的几乎匹配的下界。对于后者,我们给出了学习一元类的平均查询复杂度下界。我们得到了学习幺正2设计的指数下界和学习haar随机幺正设计的双指数下界。最后,我们通过应用我们的学习算法来攻击使用经典读出量子物理不可克隆函数的身份验证协议,证明了我们的访问模型的实际相关性,部分解决了量子硬件安全中的一个重要开放问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Quantum Processes with Quantum Statistical Queries
In this work, we initiate the study of learning quantum processes from quantum statistical queries. We focus on two fundamental learning tasks in this new access model: shadow tomography of quantum processes and process tomography with respect to diamond distance. For the former, we present an efficient average-case algorithm along with a nearly matching lower bound with respect to the number of observables to be predicted. For the latter, we present average-case query complexity lower bounds for learning classes of unitaries. We obtain an exponential lower bound for learning unitary 2-designs and a doubly exponential lower bound for Haar-random unitaries. Finally, we demonstrate the practical relevance of our access model by applying our learning algorithm to attack an authentication protocol using Classical-Readout Quantum Physically Unclonable Functions, partially addressing an important open question in quantum hardware security.
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来源期刊
Quantum
Quantum Physics and Astronomy-Physics and Astronomy (miscellaneous)
CiteScore
9.20
自引率
10.90%
发文量
241
审稿时长
16 weeks
期刊介绍: Quantum is an open-access peer-reviewed journal for quantum science and related fields. Quantum is non-profit and community-run: an effort by researchers and for researchers to make science more open and publishing more transparent and efficient.
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