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arXiv · 1709.05468

Machine learning technique to find quantum many-body ground states of bosons on a lattice

Hiroki Saito·Masaya Kato
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Abstract

We develop a variational method to obtain many-body ground states of the Bose-Hubbard model using feedforward artificial neural networks. A fully-connected network with a single hidden layer works better than a fully-connected network with multiple hidden layers, and a multi-layer convolutional network is more efficient than a fully-connected network. AdaGrad and Adam are optimization methods that work well. Moreover, we show that many-body ground states with different numbers of atoms can be generated by a single network.

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cond-mat.dis-nncond-mat.quant-gasquant-ph

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Hiroki Saito, Masaya Kato. 2017-09-16. Machine learning technique to find quantum many-body ground states of bosons on a lattice. https://doi.org/10.7566/jpsj.87.014001

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