Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Correlation-Enhanced Neural Networks as Interpretable Variational
  Quantum States   Variational methods have proven to be excellent tools to approximate ground
states of complex many body Hamiltonians. Generic tools like neural networks
are extremely powerful, but their parameters are not necessarily physically
motivated. Thus, an efficient parametrization of the wave-function can become
challenging. In this letter we introduce a neural-network based variational
ansatz that retains the flexibility of these generic methods while allowing for
a tunability with respect to the relevant correlations governing the physics of
the system. We illustrate the success of this approach on topological,
long-range correlated and frustrated models. Additionally, we introduce
compatible variational optimization methods for exploration of low-lying
excited states without symmetries that preserve the interpretability of the
ansatz.

25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
2
The categories of this paper are: math
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th