Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Style-based quantum generative adversarial networks for Monte Carlo
  events   We propose and assess an alternative quantum generator architecture in the
context of generative adversarial learning for Monte Carlo event generation,
used to simulate particle physics processes at the Large Hadron Collider (LHC).
We validate this methodology by implementing the quantum network on artificial
data generated from known underlying distributions. The network is then applied
to Monte Carlo-generated datasets of specific LHC scattering processes. The new
quantum generator architecture leads to an improvement in state-of-the-art
implementations while maintaining shallow-depth networks. Moreover, the quantum
generator successfully learns the underlying distribution functions even if
trained with small training sample sets; this is particularly interesting for
data augmentation applications. We deploy this novel methodology on two
different quantum hardware architectures, trapped-ion and superconducting
technologies, to test its hardware-independent viability.

11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph