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Learning phase transitions from dynamics

Evert van Nieuwenburg, Eyal Bairey, and Gil Refael
Phys. Rev. B 98, 060301(R) – Published 9 August 2018
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Abstract

We propose the use of recurrent neural networks for classifying phases of matter based on the dynamics of experimentally accessible observables. We demonstrate this approach by training recurrent networks on the magnetization traces of two distinct models of one-dimensional disordered and interacting spin chains. The obtained phase diagram for a well-studied model of the many-body localization transition shows excellent agreement with previously known results obtained from time-independent entanglement spectra. For a periodically driven model featuring an inherently dynamical time-crystalline phase, the phase diagram that our network traces coincides with an order parameter for its expected phases.

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  • Received 23 December 2017
  • Revised 10 April 2018

DOI:https://doi.org/10.1103/PhysRevB.98.060301

©2018 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & ThermodynamicsGeneral Physics

Authors & Affiliations

Evert van Nieuwenburg1, Eyal Bairey2, and Gil Refael1

  • 1Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125, USA
  • 2Physics Department, Technion, Haifa 3200003, Israel

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Issue

Vol. 98, Iss. 6 — 1 August 2018

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