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Solving the Bose-Hubbard model with machine learning

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arxiv 1707.09723 v1 pith:DICGPNSC submitted 2017-07-31 cond-mat.dis-nn cond-mat.quant-gas

Hiroki Saito This is my paper
classification cond-mat.dis-nncond-mat.quant-gas
keywords quantumbose-hubbardmany-bodymethodmodelneuralproblemssolving
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Neural Wavefunctions in Quantum Field Theory I: Asymptotic Freedom

    hep-lat 2026-06 unverdicted novelty 6.0 of 10

    Neural network wavefunctions enable variational calculations that reproduce asymptotic freedom, dynamical mass generation, and step-scaling in the 2D nonlinear sigma model.

  2. Neural Wavefunctions in Quantum Field Theory I: Asymptotic Freedom

    hep-lat 2026-06 unverdicted novelty 6.0 of 10

    Neural-network wavefunctions enable variational Monte Carlo calculations that reproduce asymptotic freedom, dynamical mass generation, and the step-scaling function in the 2D nonlinear sigma-model.

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