  Tremendous progress has been witnessed in artificial intelligence where
neural network backed deep learning systems have been used, with applications
in almost every domain. As a representative deep learning framework, Generative
Adversarial Network (GAN) has been widely used for generating artificial
images, text-to-image or image augmentation across areas of science, arts and
video games. However, GANs are computationally expensive, sometimes
computationally prohibitive. Furthermore, training GANs may suffer from
convergence failure and modal collapse. Aiming at the acceleration of use cases
for practical quantum computers, we propose QuGAN, a quantum GAN architecture
that provides stable convergence, quantum-state based gradients and
significantly reduced parameter sets. The QuGAN architecture runs both the
discriminator and the generator purely on quantum state fidelity and utilizes
the swap test on qubits to calculate the values of quantum-based loss
functions. Built on quantum layers, QuGAN achieves similar performance with a
94.98% reduction on the parameter set when compared to classical GANs. With the
same number of parameters, additionally, QuGAN outperforms state-of-the-art
quantum based GANs in the literature providing a 48.33% improvement in system
performance compared to others attaining less than 0.5% in terms of similarity
between generated distributions and original data sets. QuGAN code is released
at https://github.com/yingmao/Quantum-Generative-Adversarial-Network
