mlsanityData to 7 Oct 2026

Quantum Denoising Diffusion Models

arXiv 2401.07049 ↗First posted 13 Jan 2024quant-ph cs.CVcited by 11

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions. Concurrently, quantum computing is showing promising advances, especially with quantum machine learning which capitalizes on quantum mechanics to meet the increasing computational requirements of traditional machine learning algorithms. This paper explores the integration of quantum machine learning and variational quantum circuits to augment the efficacy of diffusion-based image generation models. Specifically, we address two challenges of classical diffusion models: their low sampling speed and the extensive parameter requirements. We introduce two quantum diffusion models and benchmark their capabilities against their classical counterparts using MNIST digits, Fashion MNIST, and CIFAR-10. Our models surpass the classical models with similar parameter counts in terms of performance metrics FID, SSIM, and PSNR. Moreover, we introduce a consistency model unitary single sampling architecture that combines the diffusion procedure into a single step, enabling a fast one-step image generation.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 25 of its 54 references that are in the map. First posted on arXiv 13 Jan 2024.

Builds on

25 of 54 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Improving Diffusion Model Efficiency Through Patching9 Jul
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  3. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  4. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  5. 2020Denoising Diffusion Implicit Models6 Oct
  6. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  6. 2014Going Deeper with Convolutionscited by 4,231
  7. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  8. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  9. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  10. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  11. 2015Deep Learning with Limited Numerical Precisioncited by 368
  12. 2015Recent Advances in Convolutional Neural Networkscited by 360
7 more
  1. 2016The Importance of Skip Connections in Biomedical Image Segmentationcited by 126
  2. 2018Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removalcited by 56
  3. 2018An Introduction to Image Synthesis with Generative Adversarial Netscited by 32
  4. 2018An Improved Evaluation Framework for Generative Adversarial Networkscited by 26
  5. 2016Accelerating Deep Convolutional Networks using low-precision and sparsitycited by 8
  6. 2022Improving Convergence for Quantum Variational Classifiers using Weight Re-Mappingcited by 8
  7. 2020The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipelinecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 6.

11 papers.

  1. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 12
  2. 2025Quantum-enhanced Computer Vision: Going Beyond Classical Algorithmscited by 5
  3. 2024Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splittingcited by 4
  4. 2025Quantum Generative Models for Image Generation: Insights from MNIST and MedMNISTcited by 4
  5. 2026Local-Time Riemannian Score Matching on the Quantum Pure-State Manifoldcited by 1
  6. 2024Quantum Diffusion Models for Few-Shot Learningcited by 0
  7. 2026Quantum King-Ring Domination in Chess: A QAOA Approachcited by 0
  8. 2026Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transportcited by 0
  9. 2026An Hybrid Quantum-Classical Diffusion Model for Image Generationcited by 0
  10. 2026Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failurescited by 0
  11. 2026Quantum MeanFlow: single-shot generative sampling on NISQ hardwarecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Quantum circuit synthesis with diffusion models
  2. 2023Quantum-Noise-Driven Generative Diffusion Models
  3. 2023Generative quantum machine learning via denoising diffusion probabilistic models
  4. 2025Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
  5. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
  6. 2025Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
  7. 2024Mixed-State Quantum Denoising Diffusion Probabilistic Model
  8. 2026Quantum Diffusion Models for Medical Image Analysis