From Bloch Oscillations to Many Body Localization in Clean Interacting Systems

TL;DR

This paper shows that non-random mechanisms like linear potentials can induce many-body localization in interacting systems without disorder, expanding understanding of localization phenomena beyond traditional disordered models.

Contribution

It demonstrates that linear potential gradients can cause many-body localization in clean interacting systems, introducing a new class of non-random localized models and applying machine learning for spectral analysis.

Findings

Localization persists beyond a critical potential gradient

Models exhibit non-ergodic spectral and dynamical behavior

Machine learning enables large-scale level statistics analysis

Abstract

In this work we demonstrate that non-random mechanisms that lead to single-particle localization may also lead to many-body localization, even in the absence of disorder. In particular, we consider interacting spins and fermions in the presence of a linear potential. In the non-interacting limit, these models show the well known Wannier-Stark localization. We analyze the fate of this localization in the presence of interactions. Remarkably, we find that beyond a critical value of the potential gradient, these models exhibit non-ergodic behavior as indicated by their spectral and dynamical properties. These models, therefore, constitute a new class of generic non-random models that fail to thermalize. As such, they suggest new directions for experimentally exploring and understanding the phenomena of many-body localization. We supplement our work by showing that by employing machine learning techniques, the level statistics of a system may be calculated without generating and diagonalizing the Hamiltonian, which allows a generation of large statistics.