  Recently, there has been significant progress in solving quantum
many-particle problem via machine learning based on the restricted Boltzmann
machine. However, it is still highly challenging to solve frustrated models via
machine learning, which has not been demonstrated so far. In this work, we
design a brand new convolutional neural network (CNN) to solve such quantum
many-particle problems. We demonstrate, for the first time, of solving the
highly frustrated spin-1/2 J$_1$-J$_2$ antiferromagnetic Heisenberg model on
square lattices via CNN. The energy per site achieved by the CNN is even better
than previous string-bond-state calculations. Our work therefore opens up a new
routine to solve challenging frustrated quantum many-particle problems using
machine learning.
