Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Solving frustrated quantum many-particle models with convolutional
  neural networks   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.

20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
19
The categories of this paper are: eess
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph