Robust and efficient verification of graph states in blind measurement-based quantum computation

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED npj Quantum Information Pub Date : 2023-11-14 DOI:10.1038/s41534-023-00783-9
Zihao Li, Huangjun Zhu, Masahito Hayashi
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Abstract

Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. Measurement-based quantum computation (MBQC) is a promising approach for realizing BQC. To obtain reliable results in blind MBQC, it is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. However, previous verification protocols for this task are too resource-consuming or noise-susceptible to be applied in practice. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension in the adversarial scenario, which leads to a robust and efficient protocol for verifying the resource state in blind MBQC. Our protocol requires only local Pauli measurements and is thus easy to realize with current technologies. Nevertheless, it can achieve optimal scaling behaviors with respect to the system size and the target precision as quantified by the infidelity and significance level, which has never been achieved before. Notably, our protocol can exponentially enhance the scaling behavior with the significance level.

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基于盲测量的量子计算中图形状态的鲁棒高效验证
盲量子计算(Blind quantum computing, BQC)是一种保护客户端隐私的安全量子计算方法。基于测量的量子计算(MBQC)是实现量子质量控制的一种很有前途的方法。为了在盲MBQC中获得可靠的结果,验证在对抗场景中资源图状态是否准确制备是至关重要的。然而,现有的验证协议在实际应用中存在资源消耗大、噪声影响大等问题。在此,我们提出了一种鲁棒和高效的协议来验证对抗场景中具有任意素数局部维的任意图状态,从而为盲MBQC中的资源状态验证提供了一种鲁棒和高效的协议。我们的方案只需要局部泡利测量,因此用现有技术很容易实现。然而,它可以在系统大小和目标精度方面实现最优的缩放行为,通过不忠和显著性水平量化,这是以前从未实现过的。值得注意的是,我们的协议可以通过显著性水平指数增强缩放行为。
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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.
期刊最新文献
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