论文标题

使用张量网态用于多粒子布朗棘轮

Using tensor network states for multi-particle Brownian ratchets

论文作者

Strand, Nils E., Vroylandt, Hadrien, Gingrich, Todd R.

论文摘要

对布朗棘轮的研究已经教授了时间周期性的驾驶如何支持产生非平衡传输的时间周期稳态。当单个粒子以一个维度运输时,就可以根据电位合理化电流,但是实验努力已经冒险超越了单身案例,向具有许多相互作用的载体的系统。使用一个维度的体积排除粒子的晶格模型,我们分析了相互作用对闪烁棘轮电流的影响。为了克服多体问题,我们使用二进制树张量网络采用时间依赖性的变分原理,在同伴论文中详细讨论。张量网络方法不是传播单个轨迹,而是通过可控的变分近似在多体配置上传播分布。随着晶格占用率的增加,该计算重现了吉莱斯皮轨迹采样,识别和解释最大电流频率向更高驱动频率的变化。

The study of Brownian ratchets has taught how time-periodic driving supports a time-periodic steady state that generates nonequilibrium transport. When a single particle is transported in one dimension, it is possible to rationalize the current in terms of the potential, but experimental efforts have ventured beyond that single-body case to systems with many interacting carriers. Working with a lattice model of volume-excluding particles in one dimension, we analyze the impact of interactions on a flashing ratchet's current. To surmount the many-body problem, we employ the time-dependent variational principle with a binary tree tensor network, methods discussed at length in a companion paper. Rather than propagating individual trajectories, the tensor network approach propagates a distribution over many-body configurations via a controllable variational approximation. The calculations, which reproduce Gillespie trajectory sampling, identify and explain a shift in the frequency of maximum current to higher driving frequency as the lattice occupancy increases.

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