Restricted Boltzmann machine learning for solving strongly correlated quantum systems

Yusuke Nomura, Andrew S. Darmawan, Youhei Yamaji, Masatoshi Imada

研究成果: Article査読

127 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号205152
ジャーナルPhysical Review B
96
20
DOI
出版ステータスPublished - 2017 11月 29
外部発表はい

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学

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