Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boson sampling discrete solitons by quantum machine learning   We use a neural network variational ansatz to compute Gaussian quantum
discrete solitons in an array of waveguides described by the quantum discrete
nonlinear Schroedinger equation. By training the quantum machine learning model
in the phase space, we find different quantum soliton solutions varying the
number of particles and interaction strength. The use of Gaussian states
enables measuring the degree of entanglement and the boson sampling patterns.
We compute the probability of generating different particle pairs when varying
the soliton features and unveil that bound states of discrete solitons emit
correlated pairs of photons. These results may have a role in boson sampling
experiments with nonlinear systems and in developing quantum processors to
generate entangled many-photon nonlinear states.

21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con