  In this work, we propose IQGAN, a quantum Generative Adversarial Network
(GAN) framework for multiqubit image synthesis that can be efficiently
implemented on Noisy Intermediate Scale Quantum (NISQ) devices. We investigate
the reasons for the inferior generative performance of current quantum GANs in
our preliminary study and conclude that an adjustable input encoder is the key
to ensuring high-quality data synthesis. We then propose the IQGAN architecture
featuring a trainable multiqubit quantum encoder that effectively embeds
classical data into quantum states. Furthermore, we propose a compact quantum
generator that significantly reduces the design cost and circuit depth on NISQ
devices. Experimental results on both IBM quantum processors and quantum
simulators demonstrated that IQGAN outperforms state-of-the-art quantum GANs in
qualitative and quantitative evaluation of the generated samples, model
convergence, and quantum computing cost.
