Solving frustrated quantum many-particle models with convolutional neural networks

Recently, there has been significant progress in solving quantum many-particle problems 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 paper, we design a brand new convolutional
neural network (CNN) to solve such quantum many-particle problems. We demonstrate, for the first time, 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.