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利用克劳斯算子在含噪声中等规模量子(NISQ)设备上实现精确的非马尔可夫量子动力学。

Exact Non-Markovian Quantum Dynamics on the NISQ Device Using Kraus Operators.

作者信息

Seneviratne Avin, Walters Peter L, Wang Fei

机构信息

Department of Physics and Astronomy, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, United States.

Department of Chemistry and Biochemistry, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, United States.

出版信息

ACS Omega. 2024 Feb 15;9(8):9666-9675. doi: 10.1021/acsomega.3c09720. eCollection 2024 Feb 27.

Abstract

The theory of open quantum systems has many applications ranging from simulating quantum dynamics in condensed phases to better understanding quantum-enabled technologies. At the center of theoretical chemistry are the developments of methodologies and computational tools for simulating charge and excitation energy transfer in solutions, biomolecules, and molecular aggregates. As a variety of these processes display non-Markovian behavior, classical computer simulation can be challenging due to exponential scaling with existing methods. With quantum computers holding the promise of efficient quantum simulations, in this paper, we present a new quantum algorithm based on Kraus operators that capture the exact non-Markovian effect at a finite temperature. The implementation of the Kraus operators on the quantum machine uses a combination of singular value decomposition (SVD) and optimal Walsh operators that result in shallow circuits. We demonstrate the feasibility of the algorithm by simulating the spin-boson dynamics and the exciton transfer in the Fenna-Matthews-Olson (FMO) complex. The NISQ results show very good agreement with the exact ones.

摘要

开放量子系统理论有许多应用,从模拟凝聚相中的量子动力学,到更好地理解量子技术。理论化学的核心是开发用于模拟溶液、生物分子和分子聚集体中电荷和激发能转移的方法和计算工具。由于这些过程中的许多都表现出非马尔可夫行为,经典计算机模拟因现有方法的指数级缩放而具有挑战性。鉴于量子计算机有望实现高效的量子模拟,在本文中,我们提出了一种基于克劳斯算子的新量子算法,该算法能在有限温度下捕捉精确的非马尔可夫效应。在量子计算机上实现克劳斯算子使用了奇异值分解(SVD)和最优沃尔什算子的组合,从而得到浅电路。我们通过模拟自旋 - 玻色子动力学和费纳 - 马修斯 - 奥尔森(FMO)复合物中的激子转移来证明该算法的可行性。含噪声中等规模量子(NISQ)结果与精确结果显示出非常好的一致性。

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