Qudit Shadow Estimation Based on the Clifford Group and the Power of a Single Magic Gate

IF 9 1区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Physical review letters Pub Date : 2025-04-21 DOI:10.1103/physrevlett.134.160801
Chengsi Mao, Changhao Yi, Huangjun Zhu
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Abstract

Shadow estimation is a sample-efficient protocol for learning the properties of a quantum system using randomized measurements, but the current understanding of qudit shadow estimation is quite limited compared with the qubit setting. Here, we clarify the sample complexity of qudit shadow estimation based on the Clifford group, where the local dimension d is an odd prime. Notably, we show that the overhead of qudit shadow estimation over the qubit counterpart is only O(d), which is independent of the qudit number n, although the set of stabilizer states may deviate exponentially from a 3-design with respect to the third moment operator. Furthermore, by adding one layer of magic gates, we propose a simple circuit that can significantly boost the efficiency. Actually, a single magic gate can already eliminate the O(d) overhead in qudit shadow estimation and bridge the gap from the qubit setting. Published by the American Physical Society 2025
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基于Clifford群和单魔门威力的Qudit阴影估计
阴影估计是一种使用随机测量来学习量子系统特性的样本效率协议,但与量子比特设置相比,目前对量子比特阴影估计的理解相当有限。在这里,我们澄清了基于Clifford群的qudit阴影估计的样本复杂度,其中局部维数d是奇素数。值得注意的是,我们证明了量子比特阴影估计的开销仅为O(d),这与量子比特数n无关,尽管相对于第三矩算子,稳定状态集可能会以指数方式偏离3-设计。此外,通过增加一层魔门,我们提出了一个简单的电路,可以显着提高效率。实际上,一个魔法门已经可以消除量子比特阴影估计中的O(d)开销,并弥合与量子比特设置的差距。2025年由美国物理学会出版
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来源期刊
Physical review letters
Physical review letters 物理-物理:综合
CiteScore
16.50
自引率
7.00%
发文量
2673
审稿时长
2.2 months
期刊介绍: Physical review letters(PRL)covers the full range of applied, fundamental, and interdisciplinary physics research topics: General physics, including statistical and quantum mechanics and quantum information Gravitation, astrophysics, and cosmology Elementary particles and fields Nuclear physics Atomic, molecular, and optical physics Nonlinear dynamics, fluid dynamics, and classical optics Plasma and beam physics Condensed matter and materials physics Polymers, soft matter, biological, climate and interdisciplinary physics, including networks
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