  A machine learning technique to obtain the ground states of quantum few-body
systems using artificial neural networks is developed. Bosons in continuous
space are considered and a neural network is optimized in such a way that when
particle positions are input into the network, the ground-state wave function
is output from the network. The method is applied to the Calogero-Sutherland
model in one-dimensional space and Efimov bound states in three-dimensional
space.
