  Deep Neural Networks (DNNs) come into the limelight in High Energy Physics
(HEP) in order to manipulate the increasing amount of data encountered in the
next generation of accelerators. Recently, the HEP community has suggested
Generative Adversarial Networks (GANs) to replace traditional time-consuming
Geant4 simulations based on the Monte Carlo method. In parallel with advances
in deep learning, intriguing studies have been conducted in the last decade on
quantum computing, including the Quantum GAN model suggested by IBM. However,
this model is limited in learning a probability distribution over discrete
variables, while we initially aim to reproduce a distribution over continuous
variables in HEP. We introduce and analyze a new prototype of quantum GAN
(qGAN) employed in continuous-variable (CV) quantum computing, which encodes
quantum information in a continuous physical observable. Two CV qGAN models
with a quantum and a classical discriminator have been tested to reproduce
calorimeter outputs in a reduced size, and their advantages and limitations are
discussed.
