mlsanityData to 7 Oct 2026

Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model

arXiv 2602.22061 ↗First posted 25 Feb 2026quant-ph cs.LG nlin.CDcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics. Quantum denoising diffusion probabilistic models (QuDDPMs) enable efficient learning of quantum data distributions by progressively scrambling and denoising quantum states. However, existing implementations typically rely on circuit-based random unitary dynamics, which can be costly to implement and sensitive to control imperfections, particularly on analog quantum hardware. We propose the chaotic quantum diffusion model, a framework that generates projected ensembles via chaotic Hamiltonian time evolution, providing a flexible and hardware-compatible diffusion mechanism. Requiring only global, time-independent control, our approach substantially reduces implementation overhead across diverse analog quantum platforms while achieving accuracy comparable to QuDDPMs. This method improves trainability and robustness, broadening the applicability of quantum generative modeling.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 10 of its 51 references that are in the map. First posted on arXiv 25 Feb 2026.

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10 of 51 references.

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Same direction, newest first

  1. 2023Generative quantum machine learning via denoising diffusion probabilistic models9 Oct
  2. 2023Quantum-Noise-Driven Generative Diffusion Models23 Aug

Other references · 8

  1. 2018Barren plateaus in quantum neural network training landscapescited by 624
  2. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
  3. 2020Experimental Quantum Generative Adversarial Networks for Image Generationcited by 83
  4. 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
  5. 2025A Primer on Quantum Machine Learningcited by 9
  6. 2025Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generationcited by 3
  7. 2026Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANscited by 2
  8. 2025Learning quantum many-body data locally: A provably scalable frameworkcited by 1

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  3. 2026An Hybrid Quantum-Classical Diffusion Model for Image Generation
  4. 2025Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
  5. 2024Quantum Denoising Diffusion Models
  6. 2025Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
  7. 2023Quantum circuit synthesis with diffusion models
  8. 2025Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion