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arXiv 2201.12062quant-phmath.DS
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量子系统的Koopman分析

Koopman analysis of quantum systems

Stefan Klus, Feliks Nüske, Sebastian Peitz

摘要:

Koopman算子理论已经成功地应用于流体动力学、分子动力学、气候科学、工程学和生物学等各个研究领域的问题。应用包括检测亚稳态或相干集,粗粒化,系统识别和控制。由随机微分方程驱动的动力学系统与量子力学之间存在着错综复杂的联系。在本文中,我们比较了基态变换和纳尔逊的随机力学,并展示了如何开发的数据驱动的方法近似的库普曼运营商可以用来分析量子物理问题。此外,我们利用薛定谔算子和随机控制问题之间的关系,表明现代数据驱动的随机控制方法可以用来解决静态或非稳态薛定谔方程。我们的发现为使用数据科学最近开发的工具解决薛定谔方程开辟了一条新途径。

英文摘要:

Koopman operator theory has been successfully applied to problems from various research areas such as fluid dynamics, molecular dynamics, climate science, engineering, and biology. Applications include detecting metastable or coherent sets, coarse-graining, system identification, and control. There is an intricate connection between dynamical systems driven by stochastic differential equations and quantum mechanics. In this paper, we compare the ground-state transformation and Nelson's stochastic mechanics and demonstrate how data-driven methods developed for the approximation of the Koopman operator can be used to analyze quantum physics problems. Moreover, we exploit the relationship between Schrödinger operators and stochastic control problems to show that modern data-driven methods for stochastic control can be used to solve the stationary or imaginary-time Schrödinger equation. Our findings open up a new avenue towards solving Schrödinger's equation using recently developed tools from data science.

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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