Quantum Diffusion Models for Medical Image Analysis

2026-09-25

AI Summary

This study addresses the limitation of quantum machine learning in processing large-scale medical images due to hardware scale constraints by proposing a scalable hybrid quantum diffusion model. The method leverages discrete-time quantum walks to simulate the forward diffusion process while employing classical models for reverse denoising, thereby overcoming the device-size dependency inherent in conventional quantum generative models. This work represents the first successful application of such an architecture to grayscale, RGB, and 3D real-world medical data, with validation performed on quantum hardware. Experimental results demonstrate that the proposed model achieves performance comparable to classical discrete state-space diffusion models across three generative metrics, confirming its potential for practical applications in medical image analysis.

Abstract

Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such as superposition, entanglement and interference. In this context, we present a scalable hybrid Quantum Diffusion Model, and evaluate its use for medical image analysis. Specifically, our method is based on a Discrete-Time Quantum Walk algorithm, executed on a real quantum device, to model the forward dynamics of the diffusion model. For the backward step of the diffusion model, we devise and evaluate a classical learning model, which is used to reversely denoise the data. In contrast with other existing attempts at applying quantum machine learning for image analysis tasks, severely limited by the size of existing quantum devices, our method allows to process real-world large size medical data. In particular, we present results on grayscale and RGB images, as well as 3D volumes of moderate sizes. We benchmark our results by reproducing an alternative classical counterpart model, based on diffusion models on discrete state spaces. By doing so, we compare the generation capabilities of both models in terms of three distinct state-of-the-art metrics in the field of image generation, showing the competitive, promising results of our approach.

Problem

• Quantum Machine Learning
• Medical Image Analysis
• Quantum Diffusion Models
• Scalability

Innovation

• Quantum Diffusion Model
• Discrete-Time Quantum Walk
• Hybrid Quantum-Classical Architecture
• Medical Image Analysis
• Scalable Quantum Machine Learning