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arXiv 2301.03385quant-ph
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使用 ShadowGrouping 对量子多体哈密顿量进行有保证的高效能量估计

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

Alexander Gresch, Martin Kliesch

AI总结:

针对量子多体系统能量估计中的测量瓶颈,本文提出 ShadowGrouping 方法,结合影子估计与泡利串分组策略,利用新尾部界高效选择测量设置,在分子基态能量估计中优于现有方法。

摘要:

量子多体系统能量的估计是多个研究领域中的典型任务。特别是,高效的能量估计对于在实际相关问题中实现量子优势可能至关重要。例如,测量开销构成了变分量子算法的一个关键瓶颈。我们的目标是在给定总测量预算的情况下,寻找使用单量子比特测量能够提供最高可证明精度的最优策略。作为核心工具,我们为能量的经验估计量建立了新的尾部界。这些尾部界有助于识别最能改善能量估计的测量设置。该任务构成一个 NP 难问题。然而,我们能够绕过这一瓶颈,并利用尾部界开发出一种实用且高效的估计策略,我们称之为 ShadowGrouping。顾名思义,它将影子估计方法与泡利串的分组策略相结合。在数值实验中,我们证明 ShadowGrouping 在估计各种小分子的电子基态能量方面优于最先进的方法,无论是在可证明精度基准还是实际精度基准上。因此,这项工作提供了一种有前景的途径,例如,用于解决与量子多体哈密顿量相关的测量瓶颈。

英文摘要:

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 new 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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