Machine Learning Many-Body Localization : Search for the Elusive Nonergodic Metal

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Yi-Ting Hsu
  • Xiao Li
  • Dong-Ling Deng
  • S. Das Sarma

Detail(s)

Original languageEnglish
Article number245701
Journal / PublicationPhysical Review Letters
Volume121
Issue number24
Online published10 Dec 2018
Publication statusPublished - 14 Dec 2018
Externally publishedYes

Abstract

The breaking of ergodicity in isolated quantum systems with a single-particle mobility edge is an intriguing subject that has not yet been fully understood. In particular, whether a nonergodic but metallic phase exists or not in the presence of a one-dimensional quasiperiodic potential is currently under active debate. In this Letter, we develop a neural-network-based approach to investigate the existence of this nonergodic metallic phase in a prototype model using many-body entanglement spectra as the sole diagnostic. We find that such a method identifies with high confidence the existence of a nonergodic metallic phase in the midspectrum at an intermediate quasiperiodic potential strength. Our neural-network-based approach shows how supervised machine learning can be applied not only in locating phase boundaries but also in providing a way to definitively examine the existence or not of a novel phase.

Citation Format(s)

Machine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal. / Hsu, Yi-Ting; Li, Xiao; Deng, Dong-Ling et al.
In: Physical Review Letters, Vol. 121, No. 24, 245701, 14.12.2018.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review