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

TitleMachine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal
Publication TypeJournal Article
Year of Publication2018
AuthorsY-T. Hsu, X. Li, D-L. Deng, and S. Das Sarma
JournalPHYSICAL REVIEW LETTERS
Volume121
Pagination245701
Date PublishedDEC 10
ISSN0031-9007
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.

DOI10.1103/PhysRevLett.121.245701