论文标题
通过张量网络对二维中动态大偏差的最佳采样
Optimal sampling of dynamical large deviations in two dimensions via tensor networks
论文作者
论文摘要
我们使用预测的纠缠pair态（PEPS）来计算两个维方EAST模型的动力学活动的大偏差（LD）统计数据，以及具有开放界限的二维对称简单排除过程（SSEP），在高达40x40个位置的晶格中。我们表明，长时间，这两个模型都有活性动力学相之间的相变。对于2D East模型，我们发现此轨迹过渡是一阶的，而对于SSEP，我们发现了二阶转变的指示。然后，我们展示如何使用PEP来实现能够直接访问稀有轨迹的轨迹采样方案。我们还讨论了如何扩展此处描述的方法以在有限的时间研究罕见事件。
We use projected entangled-pair states (PEPS) to calculate the large deviations (LD) statistics of the dynamical activity of the two dimensional East model, and the two dimensional symmetric simple exclusion process (SSEP) with open boundaries, in lattices of up to 40x40 sites. We show that at long-times both models have phase transitions between active and inactive dynamical phases. For the 2D East model we find that this trajectory transition is of the first-order, while for the SSEP we find indications of a second order transition. We then show how the PEPS can be used to implement a trajectory sampling scheme capable of directly accessing rare trajectories. We also discuss how the methods described here can be extended to study rare events at finite times.
