论文标题

在开放量子系统的随机动力学中有效选择有色噪声

Efficient choice of coloured noises in stochastic dynamics of open quantum systems

论文作者

Matos, Daniel, Lane, Matthew A, Ford, Ian J, Kantorovich, Lev

论文摘要

随机Liouville-von Neumann(SLN)方程描述了开放量子系统的动力学,降低了密度矩阵与非马克维亚谐波环境耦合。与环境的相互作用由驱动系统的复杂彩色噪声表示,其相关函数由环境的属性设置。我们提出了许多能够产生这种彩色噪声的方案,这些方案是建立在噪声幅度降低程序上的[Imai等,Chem。物理。 446,134(2015)],包括两个分析优化的方案。这样一来,我们密切关注相关函数在傅立叶空间中的属性,这是我们完整得出的。对于某些方案,对反应的维也纳过滤方法导致人们意识到,降低噪声相关功能的因果关系会大大提高数值收敛,从而使我们能够引入一种控制良好的方法。我们比较了这些方案的能力,以及替代优化方案[Schmitz and Stockburger,Eur。物理。 J。:规格。顶部。 227,1929(2019)],为了降低降低密度矩阵痕迹的平均值和方差的增长,以及它们扩展动力学稳定区域且在一系列温度方面的稳定区域的能力。通过数值优化额外的噪声缩放自由,我们确定了最适合所使用参数的方案,通过数量级来改善收敛性,并增加模拟访问的时间。

The Stochastic Liouville-von Neumann (SLN) equation describes the dynamics of an open quantum system reduced density matrix coupled to a non-Markovian harmonic environment. The interaction with the environment is represented by complex coloured noises which drive the system, and whose correlation functions are set by the properties of the environment. We present a number of schemes capable of generating coloured noises of this kind that are built on a noise amplitude reduction procedure [Imai et al, Chem. Phys. 446, 134 (2015)], including two analytically optimised schemes. In doing so, we pay close attention to the properties of the correlation functions in Fourier space, which we derive in full. For some schemes the method of Wiener filtering for deconvolutions leads to the realisation that weakening causality in one of the noise correlation functions improves numerical convergence considerably, allowing us to introduce a well controlled method for doing so. We compare the ability of these schemes, along with an alternative optimised scheme [Schmitz and Stockburger, Eur. Phys. J.: Spec. Top. 227, 1929 (2019)], to reduce the growth in the mean and variance of the trace of the reduced density matrix, and their ability to extend the region in which the dynamics is stable and well converged for a range of temperatures. By numerically optimising an additional noise scaling freedom, we identify the scheme which performs best for the parameters used, improving convergence by orders of magnitude and increasing the time accessible by simulation.

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