  Generative adversarial network (GAN) is one of the widely-adopted
machine-learning frameworks for a wide range of applications such as generating
high-quality images, video, and audio contents. However, training a GAN could
become computationally expensive for large neural networks. In this work, we
propose a hybrid quantum-classical architecture for improving GAN (denoted as
QC-GAN). The performance was examed numerically by benchmarking with a
classical GAN using MindSpore Quantum on the task of hand-written image
generation. The generator of the QC-GAN consists of a quantum variational
circuit together with a one-layer neural network, and the discriminator
consists of a traditional neural network. Leveraging the entangling and
expressive power of quantum circuits, our hybrid architecture achieved better
performance (Frechet Inception Distance) than the classical GAN, with much
fewer training parameters and number of iterations for convergence. We have
also demonstrated the superiority of QC-GAN over an alternative quantum GAN,
namely pathGAN, which could hardly generate 16$\times$16 or larger images. This
work demonstrates the value of combining ideas from quantum computing with
machine learning for both areas of Quantum-for-AI and AI-for-Quantum.
