Quantum Generative Learning for High-Resolution Medical Image Generation

TL;DR

This paper introduces a quantum image generative learning approach that leverages variational quantum circuits and Wasserstein distance to produce high-quality, diverse medical images, outperforming classical and existing quantum models.

Contribution

It proposes a scalable quantum generator using principal component extraction and integrates Wasserstein distance for improved medical image generation.

Findings

Achieves lower FID scores than classical and existing QGAN models.

Demonstrates superior quality in X-ray and medical MNIST datasets.

Addresses scalability and global structure capture in quantum generative models.

Abstract

Integration of quantum computing in generative machine learning models has the potential to offer benefits such as training speed-up and superior feature extraction. However, the existing quantum generative adversarial networks (QGANs) fail to generate high-quality images due to their patch-based, pixel-wise learning approaches. These methods capture only local details, ignoring the global structure and semantic information of images. In this work, we address these challenges by proposing a quantum image generative learning (QIGL) approach for high-quality medical image generation. Our proposed quantum generator leverages variational quantum circuit approach addressing scalability issues by extracting principal components from the images instead of dividing them into patches. Additionally, we integrate the Wasserstein distance within the QIGL framework to generate a diverse set of medical samples. Through a systematic set of simulations on X-ray images from knee osteoarthritis and medical MNIST datasets, our model demonstrates superior performance, achieving the lowest Fr\'echet Inception Distance (FID) scores compared to its classical counterpart and advanced QGAN models reported in the literature.