Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum generative adversarial networks   Quantum machine learning is expected to be one of the first potential
general-purpose applications of near-term quantum devices. A major recent
breakthrough in classical machine learning is the notion of generative
adversarial training, where the gradients of a discriminator model are used to
train a separate generative model. In this work and a companion paper, we
extend adversarial training to the quantum domain and show how to construct
generative adversarial networks using quantum circuits. Furthermore, we also
show how to compute gradients -- a key element in generative adversarial
network training -- using another quantum circuit. We give an example of a
simple practical circuit ansatz to parametrize quantum machine learning models
and perform a simple numerical experiment to demonstrate that quantum
generative adversarial networks can be trained successfully.

18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn