Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
quant-ph, cs.LG, nlin.CD
Submitted: 2026-02-25
Updated: 2026-09-05
Comments: Add explanation on how to select chaotic parameters
License: http://creativecommons.org/licenses/by/4.0/
The gist: Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics.
Terminology
Abstract
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.
Sources
- Quantum Diffusion Models
- Dissipative quantum generative adversarial networks
- A Primer on Quantum Machine Learning
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- The Scrooge ensemble in many-body quantum systems
- Nature is stingy: Universality of Scrooge ensembles in quantum many-body systems
- Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation
- Latent Style-based Quantum GAN for high-quality Image Generation
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