Solving frustrated quantum many-particle models with convolutional neural networks

Understand

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.