  The potential advantage of machine learning in quantum computers is a topic
of intense discussion in the literature. Theoretical, numerical and
experimental explorations will most likely be required to understand its power.
There has been different algorithms proposed to exploit the probabilistic
nature of variational quantum circuits for generative modelling. In this paper,
we employ a hybrid architecture for quantum generative adversarial networks
(QGANs) and study their robustness in the presence of noise. We devise a simple
way of adding different types of noise to the quantum generator circuit, and
numerically simulate the noisy hybrid quantum generative adversarial networks
(HQGANs) to learn continuous probability distributions, and show that the
performance of HQGANs remain unaffected. We also investigate the effect of
different parameters on the training time to reduce the computational scaling
of the algorithm and simplify its deployment on a quantum computer. We then
perform the training on Rigetti's Aspen-4-2Q-A quantum processing unit, and
present the results from the training. Our results pave the way for
experimental exploration of different quantum machine learning algorithms on
noisy intermediate scale quantum devices.
