Multi-state quantum simulations via model-space quantum imaginary time evolution

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED npj Quantum Information Pub Date : 2023-11-07 DOI:10.1038/s41534-023-00780-y
Takashi Tsuchimochi, Yoohee Ryo, Siu Chung Tsang, Seiichiro L. Ten-no
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引用次数: 2

Abstract

We introduce the framework of model space into quantum imaginary time evolution (QITE) to enable stable estimation of ground and excited states using a quantum computer. Model-space QITE (MSQITE) propagates a model space to the exact one by retaining its orthogonality, and hence is able to describe multiple states simultaneously. The quantum Lanczos (QLanczos) algorithm is extended to MSQITE to accelerate the convergence. The present scheme is found to outperform both the standard QLanczos and the recently proposed folded-spectrum QITE in simulating excited states. Moreover, we demonstrate that spin contamination can be effectively removed by shifting the imaginary time propagator, and thus excited states with a particular spin quantum number are efficiently captured without falling into the different spin states that have lower energies. We also investigate how different levels of the unitary approximation employed in MSQITE can affect the results. The effectiveness of the algorithm over QITE is demonstrated by noise simulations for the H4 model system.

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基于模型空间量子虚时演化的多态量子模拟
我们将模型空间的框架引入量子虚时演化(QITE),以使用量子计算机稳定地估计基态和激发态。模型空间QITE(MSQITE)通过保持其正交性将模型空间传播到精确的模型空间,因此能够同时描述多个状态。将量子Lanczos(QLanczos)算法推广到MSQITE中,以加速收敛。发现本方案在模拟激发态方面优于标准QLanczos和最近提出的折叠谱QITE。此外,我们证明了通过移动虚时间传播子可以有效地去除自旋污染,从而有效地捕获具有特定自旋量子数的激发态,而不会落入具有较低能量的不同自旋态。我们还研究了MSQITE中使用的酉近似的不同水平如何影响结果。通过H4模型系统的噪声仿真,验证了该算法在QITE上的有效性。
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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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