MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers

TL;DR

MosaiQ is a novel quantum GAN framework designed for high-quality image generation on current NISQ quantum computers, addressing previous issues of poor quality and robustness in quantum image synthesis.

Contribution

This paper introduces MosaiQ, the first quantum GAN framework capable of producing high-quality images on NISQ devices, advancing quantum image generation.

Findings

Successfully generates high-quality images on NISQ hardware

Improves robustness over previous quantum image generation methods

Demonstrates feasibility of quantum GANs on near-term quantum computers

Abstract

Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with many potential advantages over non-quantum techniques; however, previous techniques have suffered from poor quality and robustness. To address these problems, we introduce, MosaiQ, a high-quality quantum image generation GAN framework that can be executed on today's Near-term Intermediate Scale Quantum (NISQ) computers.