Journal
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
Volume 87, Issue 1, Pages -Publisher
PHYSICAL SOC JAPAN
DOI: 10.7566/JPSJ.87.014001
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Funding
- JSPS KAKENHI [JP16K05505, JP17K05595, JP17K05596, JP25103007]
- Grants-in-Aid for Scientific Research [25103007, 17K05595] Funding Source: KAKEN
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We have developed 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 multilayer 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 particles can be generated by a single network.
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