# Quantum Generative Learning for High-Resolution Medical Image Generation

arXiv: https://arxiv.org/abs/2406.13196 · HTML: https://mlsanity.com/paper/2406.13196/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation](https://mlsanity.com/paper/2603.00233/) (2026)
- [A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis](https://mlsanity.com/paper/2505.14716/) (2025)
- [A Controlled Benchmark of Quantum-Latent GAN Augmentation for Brain MRI](https://mlsanity.com/paper/2606.18970/) (2026)
- [Quantum Diffusion Models for Medical Image Analysis](https://mlsanity.com/paper/2609.31070/) (2026)

## Abstract (arXiv)

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.