Unsupervised learning eigenstate phases of matter

Steven Durr and Sudip Chakravarty
Phys. Rev. B 100, 075102 – Published 1 August 2019

Abstract

Supervised learning has been successfully used to produce phase diagrams and identify phase boundaries when local order parameters are unavailable. Here, we apply unsupervised learning to this task. By using readily available clustering algorithms, we are able to extract the distinct eigenstate phases of matter within the transverse-field Ising model in the presence of interactions and disorder. We compare our results to those found through supervised learning and observe remarkable agreement. However, as opposed to the supervised procedure, our method requires no strict assumptions concerning the number of phases present, no labeled training data, and no prior knowledge of the phase diagram. We conclude with a discussion of clustering and its limitations.

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  • Received 10 May 2019
  • Revised 18 July 2019

DOI:https://doi.org/10.1103/PhysRevB.100.075102

©2019 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Steven Durr and Sudip Chakravarty

  • Mani L Bhaumik Institute for Theoretical Physics, Department of Physics and Astronomy, University of California Los Angeles, Los Angeles, California 90095, USA

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Issue

Vol. 100, Iss. 7 — 15 August 2019

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