Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual configuration segmentation of quantum states for phase
  identification in many-body systems   Artificial intelligence provides an unprecedented perspective for studying
phases of matter in condensed-matter systems. Image segmentation is a basic
technique of computer vision that belongs to a branch of artificial
intelligence. In this work, we propose a segmentation scheme named visual
configuration segmentation (VCS) to unveil quantum phases and quantum phase
transitions in many-body systems. By encoding the information of renormalized
quantum states into a color image and segmenting the color image through the
VCS, the renormalized quantum states can be visualized, from which quantum
phase transitions can be revealed and the corresponding critical points can be
identified. Our scheme is benchmarked on several strongly correlated spin
systems, which does not depend on the priori knowledge of order parameters of
quantum phases. This demonstrates the potential to disclose the underlying
structure of quantum phases by the techniques of computer vision.

34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th