Restricted Boltzmann machine learning for solving strongly correlated quantum systems

被引：239

作者：

Nomura, Yusuke ^{[1
]}

Darmawan, Andrew S. ^{[1
]}

Yamaji, Youhei ^{[1
,2
]}

Imada, Masatoshi ^{[1
]}

机构：

[1] Univ Tokyo, Dept Appl Phys, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan

[2] JST, PRESTO, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan

来源：

PHYSICAL REVIEW B | 2017年 / 96卷 / 20期

关键词：

VARIATIONAL MONTE-CARLO; ANTIFERROMAGNETIC HEISENBERG-MODEL; RENORMALIZATION-GROUP METHOD; ENTANGLED PAIR STATES; MATRIX PRODUCT STATES; MANY-BODY PROBLEM; GROUND-STATE; ELECTRON-SYSTEMS; NEURAL-NETWORKS; SQUARE LATTICE;

D O I：

10.1103/PhysRevB.96.205152

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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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页数：8

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