Generative neural samplers for the quantum Heisenberg chain

Johanna Vielhaben and Nils Strodthoff
Phys. Rev. E 103, 063304 – Published 7 June 2021

Abstract

Generative neural samplers offer a complementary approach to Monte Carlo methods for problems in statistical physics and quantum field theory. This paper tests the ability of generative neural samplers to estimate observables for real-world low-dimensional spin systems. It maps out how autoregressive models can sample configurations of a quantum Heisenberg chain via a classical approximation based on the Suzuki-Trotter transformation. We present results for energy, specific heat, and susceptibility for the isotropic XXX and the anisotropic XY chain are in good agreement with Monte Carlo results within the same approximation scheme.

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  • Received 14 January 2021
  • Revised 26 March 2021
  • Accepted 30 April 2021

DOI:https://doi.org/10.1103/PhysRevE.103.063304

©2021 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & Thermodynamics

Authors & Affiliations

Johanna Vielhaben* and Nils Strodthoff

  • Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany

  • *johanna.vielhaben@hhi.fraunhofer.de
  • nils.strodthoff@hhi.fraunhofer.de

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Issue

Vol. 103, Iss. 6 — June 2021

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