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 nonperturbative 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 noninteracting 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.

中文翻译：

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具有量子重整化群和人工神经网络的幺正长期演化

在这项工作中，我们将量子重整化群方法与深度人工神经网络相结合，以描述强无序量子物质的实时演化。我们发现这使我们能够准确计算大型多体局域系统在非微扰状态下的长期相干动力学，包括多体共振的影响。具体来说，我们使用这种方法来描述随机 Ising 链中多体局部自旋玻璃顺序的时空积累。我们观察到与非相互作用的安德森绝缘伊辛链的根本区别，其中顺序仅在有限的空间范围内发展。我们进一步将该方法应用于强无序二维 Ising 模型，