IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices
Cheng Chu, Grant Skipper, Martin Swany, Fan Chen
Abstract
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.