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[2602.22061] Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
Summary
The paper presents a novel chaotic quantum diffusion model for learning quantum data distributions, offering a more efficient and robust alternative to existing methods.
Why It Matters
This research addresses significant challenges in quantum data modeling, particularly in terms of implementation costs and control sensitivity. By proposing a chaotic Hamiltonian framework, it enhances the feasibility of quantum generative models across various platforms, which is crucial for advancements in quantum computing and related fields.
Key Takeaways
- Introduces a chaotic quantum diffusion model for quantum data distribution.
- Reduces implementation overhead compared to traditional quantum denoising methods.
- Maintains accuracy while improving robustness and trainability.
- Broadens the applicability of quantum generative modeling techniques.
- Compatible with diverse analog quantum hardware.
Quantum Physics arXiv:2602.22061 (quant-ph) [Submitted on 25 Feb 2026] Title:Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model Authors:Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima View a PDF of the paper titled Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model, by Quoc Hoan Tran and 3 other authors View PDF HTML (experimental) 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 that can be costly to realize 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. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG);...
