Unitary long-time evolution with quantum renormalization groups and artificial neural networks

TL;DR

This paper introduces a novel method combining quantum renormalization groups with neural networks to accurately simulate long-time dynamics in large, disordered quantum systems, capturing complex many-body phenomena beyond perturbative regimes.

Contribution

The work presents a new hybrid approach that enables non-perturbative, long-time evolution simulations of many-body localized systems using quantum renormalization and deep learning.

Findings

Accurately describes long-time dynamics of many-body localized systems.

Reveals differences in order development between localized and Anderson insulators.

Demonstrates applicability to 2D disordered quantum models.

Abstract

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.