Learning phase transitions from dynamics

Autor:	van Nieuwenburg, Evert, Bairey, Eyal, Refael, Gil
Rok vydání:	2017
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Strongly Correlated Electrons Quantum Physics
Zdroj:	Phys. Rev. B 98, 060301 (2018)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevB.98.060301
Popis:	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 in a previously-unexplored regime coincides with an order parameter for its expected phases. Comment: 5 pages + 3 fig, appendix + 5 fig
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.00450 Zobrazit plný text záznamu View this record from Arxiv