mlsanityData to 14 Oct 2026

Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion

arXiv 2505.22193 ↗First posted 28 May 2025quant-ph cond-mat.dis-nn cs.AIcited by 2

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical counterparts. However, existing algorithms are not easily scalable due to the limitations of near-term quantum devices. Following our previous work on QDMs, here we propose and implement two physics-inspired protocols. In the first, we use the formalism of quantum stochastic walks, showing that a specific interplay of quantum and classical dynamics in the forward process produces statistically more robust models generating sets of MNIST images with lower Fr'echet Inception Distance (FID) than using totally classical dynamics. In the second approach, we realize an algorithm to generate images by exploiting the intrinsic noise of real IBM quantum hardware with only four qubits. Our work could be a starting point to pave the way for new scenarios for large-scale algorithms in quantum Generative AI, where quantum noise is neither mitigated nor corrected, but instead exploited as a useful resource.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 27 of its 89 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

27 of 89 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble13 Jan
  2. 2023Generative quantum machine learning via denoising diffusion probabilistic models9 Oct
  3. 2023Quantum-Noise-Driven Generative Diffusion Models23 Aug
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  5. 2021Structured Denoising Diffusion Models in Discrete State-Spaces7 Jul
  6. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  7. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  8. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  5. 2022Generative Adversarial Networkscited by 5,627
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  7. 2015Variational Inference with Normalizing Flowscited by 2,170
  8. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  9. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,354
  10. 2019An Introduction to Variational Autoencoderscited by 1,000
  11. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  12. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 574
7 more
  1. 2018Quantum generative adversarial networkscited by 128
  2. 2018Quantum Variational Autoencodercited by 41
  3. 2023A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Modelscited by 16
  4. 2016Simulated Quantum Annealing Can Be Exponentially Faster than Classical Simulated Annealingcited by 11
  5. 2022A hybrid quantum image edge detector for the NISQ eracited by 8
  6. 2022Experimental quantum pattern recognition in IBMQ and diamond NVscited by 8
  7. 2024Transfer learning with generative models for object detection on limited datasetscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Quantum Generative Diffusion Model for Real-World Time Seriescited by 1
  2. 2026Quantum Diffusion Models for Medical Image Analysiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026An Hybrid Quantum-Classical Diffusion Model for Image Generation
  2. 2024Quantum Denoising Diffusion Models
  3. 2026Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
  4. 2023Quantum circuit synthesis with diffusion models
  5. 2026Enhancing Quantum Diffusion Models for Complex Image Generation
  6. 2025Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
  7. 2025Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
  8. 2024Mixed-State Quantum Denoising Diffusion Probabilistic Model