Hiroki Saito,

Solving the Bose-Hubbard model with machine learning,

Journal of the Physical Society of Japan 86, 093001/1-4 (2017).

[Summary] Motivated by the recent successful application of artificial neural networks to quantum many-body problems [G. Carleo and M. Troyer, Science 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.