Restricted Boltzmann machine learning for solving strongly correlated quantum systems

Yusuke Nomura, Andrew S. Darmawan, Youhei Yamaji, and Masatoshi Imada
Phys. Rev. B 96, 205152 – Published 29 November 2017

Abstract

We develop a machine learning method to construct accurate ground-state wave functions of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A restricted Boltzmann machine algorithm in the form of an artificial neural network is combined with a conventional variational Monte Carlo method with pair product (geminal) wave functions and quantum number projections. The combination allows an application of the machine learning scheme to interacting fermionic systems. The combined method substantially improves the accuracy beyond that ever achieved by each method separately, in the Heisenberg as well as Hubbard models on square lattices, thus proving its power as a highly accurate quantum many-body solver.

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  • Received 11 September 2017
  • Revised 14 November 2017

DOI:https://doi.org/10.1103/PhysRevB.96.205152

©2017 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsQuantum Information, Science & TechnologyInterdisciplinary PhysicsNetworks

Authors & Affiliations

Yusuke Nomura1,*, Andrew S. Darmawan1, Youhei Yamaji1,2, and Masatoshi Imada1

  • 1Department of Applied Physics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
  • 2JST, PRESTO, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

  • *nomura@ap.t.u-tokyo.ac.jp

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Issue

Vol. 96, Iss. 20 — 15 November 2017

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