Solving the Bose-Hubbard model with machine learning
Disordered Systems and Neural Networks
2017-08-01 v1 Quantum Gases
Abstract
Motivated by the recent successful application of artificial neural networks to quantum many-body problems [G. Carleo and M. Troyer, Science {\bf 355}, 602 (2017)], a method to calculate the ground state of the Bose-Hubbard model using a feedforward neural network is proposed. The results are in good agreement with those obtained by exact diagonalization and the Gutzwiller approximation. The method of neural-network quantum states is promising for solving quantum many-body problems of ultracold atoms in optical lattices.
Cite
@article{arxiv.1707.09723,
title = {Solving the Bose-Hubbard model with machine learning},
author = {Hiroki Saito},
journal= {arXiv preprint arXiv:1707.09723},
year = {2017}
}
Comments
4 pages, 4 figures