论文标题
通过张量网络状态的时间演变计算时间周期性稳态电流
Computing time-periodic steady-state currents via the time evolution of tensor network states
论文作者
论文摘要
我们提出了一种基于二进制树张量网络（BTTN）状态的方法，用于计算以体积排除相互作用的许多粒子1D棘轮的稳态统计。棘轮颗粒在具有时间周期性驱动的周期性边界条件上移动的棘轮颗粒可以随时间随时间演化，以通过吉莱斯皮方法进行样品代表性轨迹进行样本。 BTTN状态代替生成轨迹的实现，可以在大量多体配置上变化近似分布。我们将密度矩阵重新归一化组（DMRG）算法应用于初始化BTTN状态，然后通过时间依赖性变分原理（TDVP）算法在时间上传播，以产生稳态行为，包括典型和稀有轨迹的影响。在同伴字母中突出显示了该方法在棘轮电流中的应用，但是该方法自然而然地扩展到与时间有关的其他相互作用的晶格模型。尽管轨迹采样在概念和计算上更简单，但我们讨论了BTTN TDVP策略可能更有利的情况。
We present an approach based upon binary tree tensor network (BTTN) states for computing steady-state current statistics for a many-particle 1D ratchet subject to volume exclusion interactions. The ratcheted particles, which move on a lattice with periodic boundary conditions subject to a time-periodic drive, can be stochastically evolved in time to sample representative trajectories via a Gillespie method. In lieu of generating realizations of trajectories, a BTTN state can variationally approximate a distribution over the vast number of many-body configurations. We apply the density matrix renormalization group (DMRG) algorithm to initialize BTTN states, which are then propagated in time via the time-dependent variational principle (TDVP) algorithm to yield the steady-state behavior, including the effects of both typical and rare trajectories. The application of the methods to ratchet currents is highlighted in a companion letter, but the approach extends naturally to other interacting lattice models with time-dependent driving. Though trajectory sampling is conceptually and computationally simpler, we discuss situations for which the BTTN TDVP strategy could be more favorable.
