Temen Story, Akimov Alexey V
Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States.
J Phys Chem Lett. 2021 Jan 21;12(2):850-860. doi: 10.1021/acs.jpclett.0c03428. Epub 2021 Jan 11.
We present a new state tracking algorithm based on a stochastic state reassignment that reflects the quantum mechanical interpretation of the state time-overlaps. We assess the new method with a range of model Hamiltonians and demonstrate that it yields the results generally consistent with the deterministic min-cost algorithm. However, the stochastic state tracking algorithm reduces magnitudes of the state population fluctuations as the quantum system evolves toward its equilibrium. The new algorithm facilitates the thermalization of quantum state populations and suppresses the population revivals and oscillations near the equilibrium in many-state systems. The new stochastic algorithm has a favorable computational scaling, is easy to implement due to its conceptual transparency, and treats various types of state identity changes (trivial or avoided crossings and any intermediate cases) on equal footing.
我们提出了一种基于随机态重新分配的新状态跟踪算法，该算法反映了态时间重叠的量子力学解释。我们用一系列模型哈密顿量评估了这种新方法，并证明它产生的结果通常与确定性最小成本算法一致。然而，随着量子系统向其平衡态演化，随机状态跟踪算法降低了态占据数涨落的幅度。新算法促进了量子态占据数的热化，并抑制了多体系统在平衡态附近的占据数复苏和振荡。这种新的随机算法具有良好的计算尺度，由于其概念清晰而易于实现，并且平等对待各种类型的态身份变化（平凡或避免交叉以及任何中间情况）。
