IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices

Understand

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.