Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visualizing Quantum Phases And Identifying Quantum Phase Transitions By
  Nonlinear Dimensionality Reduction   Identifying quantum phases and phase transitions is key to understand complex
phenomena in statistical physics. In this work, we propose an unconventional
strategy to access quantum phases and phase transitions by visualization based
on the distribution of ground states in Hilbert space. By mapping the quantum
states in Hilbert space onto a two-dimensional feature space using an
unsupervised machine learning method, distinct phases can be directly specified
and quantum phase transitions can be well identified. Our proposal is
benchmarked on gapped, critical, and topological phases in several strongly
correlated spin systems. As this proposal directly learns quantum phases and
phase transitions from the distributions of the quantum states, it does not
require priori knowledge of order parameters of physical systems, which thus
indicates a perceptual route to identify quantum phases and phase transitions
particularly in complex systems by visualization through learning.

36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex