• Open Access

Neural-network quantum states at finite temperature

Naoki Irikura and Hiroki Saito
Phys. Rev. Research 2, 013284 – Published 9 March 2020

Abstract

We propose a method to obtain the thermal-equilibrium density matrix of a many-body quantum system using artificial neural networks. The variational function of the many-body density matrix is represented by a convolutional neural network with two input channels. We first prepare an infinite-temperature state, and the temperature is lowered by imaginary-time evolution. We apply this method to the one-dimensional Bose-Hubbard model and compare the results with those obtained by exact diagonalization.

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  • Received 7 November 2019
  • Accepted 21 February 2020

DOI:https://doi.org/10.1103/PhysRevResearch.2.013284

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

  1. Research Areas
Interdisciplinary Physics

Authors & Affiliations

Naoki Irikura and Hiroki Saito

  • Department of Engineering Science, University of Electro-Communications, Tokyo 182-8585, Japan

Article Text

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Issue

Vol. 2, Iss. 1 — March - May 2020

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