量子模拟中绝热态制备和时间演化的序列层次截断(SeqHT

IF 5.1 2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY
Quantum Pub Date : 2025-09-29 DOI:10.22331/q-2025-09-29-1865
Zhiyao Li, Dorota M. Grabowska, Martin J. Savage
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引用次数: 0

摘要

为了减少量子模拟中状态准备和时间演化所需的资源,我们引入了基于序列截断的序列层次截断(SeqHT)方案。对于标量场理论中的$\lambda\phi^4$相互作用,或任何具有多项式展开的相互作用,导出了给定序列的算子贡献的上界。对于我们所研究的系统,在序列截断波函数中计算的可观测值,包括通过魔术测量的量子相关性,发现随着截止序列的增加逐步收敛到它们的精确值。利用IBM的量子计算机$\texttt{ibm_sherbrooke}$在$\lambda\phi^4$非谐振子基态的绝热状态制备中证明了SeqHT的效用。使用SeqHT,所需量子电路的深度降低了$\sim 30\%$,从而显著改善了量子模拟中可观测值的确定。更一般地说,SeqHT有望减少具有长度尺度层次结构的系统的量子模拟所需的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sequency Hierarchy Truncation (SeqHT) for Adiabatic State Preparation and Time Evolution in Quantum Simulations
We introduce the Sequency Hierarchy Truncation (SeqHT) scheme for reducing the resources required for state preparation and time evolution in quantum simulations, based upon a truncation in sequency. For the $\lambda\phi^4$ interaction in scalar field theory, or any interaction with a polynomial expansion, upper bounds on the contributions of operators of a given sequency are derived. For the systems we have examined, observables computed in sequency-truncated wavefunctions, including quantum correlations as measured by magic, are found to step-wise converge to their exact values with increasing cutoff sequency. The utility of SeqHT is demonstrated in the adiabatic state preparation of the $\lambda\phi^4$ anharmonic oscillator ground state using IBM's quantum computer $\texttt{ibm_sherbrooke}$. Using SeqHT, the depth of the required quantum circuits is reduced by $\sim 30\%$, leading to significantly improved determinations of observables in the quantum simulations. More generally, SeqHT is expected to lead to a reduction in required resources for quantum simulations of systems with a hierarchy of length scales.
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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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