Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice

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.