Burau Heiko, Heyl Markus
Max-Planck-Institut für Physik Komplexer Systeme, Nöthnitzer Straße 38, 01187 Dresden, Germany.
Phys Rev Lett. 2021 Jul 30;127(5):050601. doi: 10.1103/PhysRevLett.127.050601.
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.
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中的实时演化。我们发现，这使我们能够在非微扰区域准确计算大型多体局域系统的长时间相干动力学，包括多体共振的影响。具体而言，我们使用这种方法来描述随机伊辛链中多体局域自旋玻璃序的时空积累。我们观察到与非相互作用的安德森绝缘伊辛链存在根本差异，在后者中，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伊辛模型，突出表明我们的方法也可用于在一般情况下描述非遍历量子物质的实时动力学。
