  Generative models, and Generative Adversarial Networks (GAN) in particular,
are being studied as possible alternatives to Monte Carlo simulations. It has
been proposed that, in certain circumstances, simulation using GANs can be
sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the
dual-Parameterized Quantum Circuit(PQC) GAN, which consists of a classical
discriminator and two quantum generators which take the form of PQCs. The first
PQC learns a probability distribution over N-pixel images, while the second
generates normalized pixel intensities of an individual image for each PQC
input. With a view to HEP applications, we evaluated the dual-PQC architecture
on the task of imitating calorimeter outputs, translated into pixelated images.
The results demonstrate that the model can reproduce a fixed number of images
with a reduced size as well as their probability distribution and we anticipate
it should allow us to scale up to real calorimeter outputs.
