mlsanityData to 14 Oct 2026

Quantum Diffusion Models for Medical Image Analysis

arXiv 2609.31070 ↗First posted 25 Sep 2026eess.IV cs.AI cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 31 of its 68 references that are in the map. First posted on arXiv 25 Sep 2026.

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31 of 68 references.

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  1. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions10 Feb

Other references · 30

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  5. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  6. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  7. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  8. 2021Quantum advantage in learning from experimentscited by 188
  9. 2023Generative quantum machine learning via denoising diffusion probabilistic modelscited by 30
  10. 2023Quantum-Noise-Driven Generative Diffusion Modelscited by 20
  11. 2023A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Modelscited by 16
  12. 2021Learning the noise fingerprint of quantum devicescited by 12
18 more
  1. 2026Exponential quantum advantage in processing massive classical datacited by 11
  2. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemblecited by 9
  3. 2022A hybrid quantum image edge detector for the NISQ eracited by 8
  4. 2022Experimental quantum pattern recognition in IBMQ and diamond NVscited by 8
  5. 2021Machine learning classification of non-Markovian noise disturbing quantum dynamicscited by 6
  6. 2023Deep learning enhanced noise spectroscopy of a spin qubit environmentcited by 6
  7. 2024Mixed-State Quantum Denoising Diffusion Probabilistic Modelcited by 6
  8. 2023The role of data embedding in equivariant quantum convolutional neural networkscited by 4
  9. 2024Quantum Generative Learning for High-Resolution Medical Image Generationcited by 4
  10. 2025Quantum Generative Models for Image Generation: Insights from MNIST and MedMNISTcited by 4
  11. 2022Noise fingerprints in quantum computers: Machine learning software toolscited by 3
  12. 2024Transfer learning with generative models for object detection on limited datasetscited by 3
  13. 2025Analysis of Quantum Image Representations for Supervised Classificationcited by 3
  14. 2025Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learningcited by 2
  15. 2025Physics-inspired Generative AI models via real hardware-based noisy quantum diffusioncited by 2
  16. 2025Continuous-variable Quantum Diffusion Model for State Generation and Restorationcited by 2
  17. 2024Machine-learning based high-bandwidth magnetic sensingcited by 1
  18. 2024Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problemcited by 1

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