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arXiv · 1707.09723

Solving the Bose-Hubbard model with machine learning

Hiroki Saito
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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.

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BibTeXRIS

Hiroki Saito. 2017-07-31. Solving the Bose-Hubbard model with machine learning. https://doi.org/10.7566/jpsj.86.093001

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