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arXiv 2603.08081quant-ph
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通过利用物理信息神经网络模拟非马尔可夫开放量子动力学

Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network

Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China(中国科学技术大学微尺度物质科学国家研究中心);State Key Laboratory of Porous Materials for Separation and Conversion & MOE Key Laboratory of Computational Physical Sciences & Department of Chemistry, Fudan University(复旦大学化学系多孔材料分离转化国家重点实验室及计算物理教育部重点实验室);Hefei National Laboratory(合肥国家实验室)

Long Cao, Liwei Ge, Daochi Zhang, Yao Wang, Rui-Xue Xu, YiJing Yan, Xiao Zheng

AI总结:

本文提出PINN-DQME方法,利用时间编码神经网络在时域分解策略中模拟由耗散嵌入量子主方程驱动的动力学,验证了其在高温下高精度模拟量子耗散动力学的能力,但在低温强非马尔可夫动力学中存在误差累积问题。

摘要:

本文通过将物理信息神经网络(PINN)方法融入神经量子态框架,模拟开放量子系统动力学,以规避传统变分方法中所需计算量大的时间依赖变分原理。所提出的PINN-DQME方法在时域分解策略中利用时间编码神经网络,以表示由耗散嵌入量子主方程(DQME)驱动的动力学演化。我们在此单杂质安德森模型中实现了该方法,并将PINN-DQME结果与数值精确的分层运动方程进行基准测试。PINN-DQME方法在高温下高精度模拟量子耗散动力学,其中非马尔可夫效应较弱。然而,在低温强非马尔可夫动力学中,该方法在时间传播过程中遇到误差累积的挑战,凸显了未来在复杂量子动力学设置中应用PINN的改进方向。

英文摘要:

This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics, to circumvent the computationally expensive time-dependent variational principle required in conventional variational methods. The proposed PINN-DQME method employs time-encoded neural networks within a time-domain decomposition strategy to represent the evolution governed by the dissipaton-embedded quantum master equation (DQME). We implement and validate this approach in the single-impurity Anderson model, benchmarking the PINN-DQME results against the numerically exact hierarchical equations of motion. The PINN-DQME method demonstrates high accuracy in simulating quantum dissipative dynamics at high temperatures, where non-Markovian effects are weak. However, for strongly non-Markovian dynamics at low temperatures, it encounters challenges with error accumulation during time propagation, highlighting an area for future refinement in applying PINNs to complex quantum dynamical settings.

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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