论文标题
量子重新归一化组和人工神经网络的统一长期演变
Unitary long-time evolution with quantum renormalization groups and artificial neural networks
论文作者
论文摘要
在这项工作中，我们将量子重新归一化组方法与深人造神经网络相结合，以描述强烈无序量子物质的实时演变。我们发现这使我们能够准确计算非扰动制度中大型多体局部系统的长期相干动力学，包括多体共振的影响。具体而言，我们使用这种方法来描述以随机的ISing链中多体局部旋转玻璃顺序的时空积累。我们观察到与非相互作用的Anderson绝缘链链的基本差异，在该链中，该顺序仅在有限的空间范围内发展。我们进一步将方法应用于强烈无序的二维Ising模型，强调我们的方法也可以用于描述一般情况下非量子量子物质的实时动力学。
In this work we combine quantum renormalization group approaches with deep artificial neural networks for the description of the real-time evolution in strongly disordered quantum matter. We find that this allows us to accurately compute the long-time coherent dynamics of large, many-body localized systems in non-perturbative regimes including the effects of many-body resonances. Concretely, we use this approach to describe the spatiotemporal buildup of many-body localized spin glass order in random Ising chains. We observe a fundamental difference to a non-interacting Anderson insulating Ising chain, where the order only develops over a finite spatial range. We further apply the approach to strongly disordered two-dimensional Ising models highlighting that our method can be used also for the description of the real-time dynamics of nonergodic quantum matter in a general context.
