  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.
