OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines

TL;DR

OrganiQ is a pioneering quantum GAN that generates high-quality images on NISQ-era quantum computers without classical neural networks, addressing resource bottlenecks and enhancing quantum image synthesis.

Contribution

It introduces the first quantum GAN capable of high-quality image generation without classical neural networks, overcoming classical resource limitations.

Findings

Successfully generates high-quality images on NISQ quantum hardware.

Eliminates reliance on classical neural networks in quantum image generation.

Addresses resource bottlenecks in quantum GANs.

Abstract

Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising results. However, prior quantum image generation techniques rely on classical neural networks, limiting their quantum potential and image quality. To overcome this, we introduce OrganiQ, the first quantum GAN capable of producing high-quality images without using classical neural networks.