Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Nature Communications Pub Date : 2025-01-15 DOI:10.1038/s41467-024-54859-x
Alexander Gresch, Martin Kliesch
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Abstract

Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. As a central tool, we establish tail bounds for empirical estimators of the energy. They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. However, we are able to circumvent this bottleneck and use the tail bounds to develop a practical, efficient estimation strategy, which we call ShadowGrouping. As the name indicates, it combines shadow estimation methods with grouping strategies for Pauli strings. In numerical experiments, we demonstrate that ShadowGrouping improves upon state-of-the-art methods in estimating the electronic ground-state energies of various small molecules, both in provable and practical accuracy benchmarks. Hence, this work provides a promising way, e.g., to tackle the measurement bottleneck associated with quantum many-body Hamiltonians.

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利用影子分组保证量子多体哈密顿的高效能量估计
量子多体系统的能量估计是各个研究领域的一个典型课题。特别是,有效的能量估计对于实现实际相关问题的量子优势至关重要。例如,测量工作是变分量子算法的一个关键瓶颈。我们的目标是找到单量子位测量的最佳策略,在给定总测量预算的情况下产生最高的可证明精度。作为一个中心工具,我们建立了能量的经验估计的尾界。它们有助于确定最能改善能量估算的测量设置。这个任务构成np困难问题。然而,我们能够绕过这个瓶颈,并使用尾部边界来开发一种实用、有效的估计策略,我们称之为ShadowGrouping。顾名思义,它将阴影估计方法与泡利字符串的分组策略相结合。在数值实验中,我们证明了ShadowGrouping在估计各种小分子的电子基态能量方面改进了最先进的方法,无论是在可证明的还是实际的精度基准上。因此,这项工作提供了一个有前途的方法,例如,解决与量子多体哈密顿量相关的测量瓶颈。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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