Quantum Diffusion Models for Medical Image Analysis

The paper introduces a scalable hybrid Quantum Diffusion Model for medical image analysis, leveraging a Discrete-Time Quantum Walk executed on a real quantum device to model forward diffusion dynamics. A classical learning model is employed for the backward denoising step, enabling the processing of large real-world medical data, including grayscale, RGB, and moderate-sized 3D volumes. The authors benchmark their quantum approach against a classical discrete-state diffusion model, demonstrating competitive generation performance across three state-of-the-art image generation metrics.

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• diffusion
• benchmarks