Unitary Scrambling and Collapse: A Quantum Diffusion Framework for Generative Modeling

• URL: http://arxiv.org/abs/2506.10571v1
• Date: Thu, 12 Jun 2025 11:00:21 GMT
• Title: Unitary Scrambling and Collapse: A Quantum Diffusion Framework for Generative Modeling
• Authors: Yihua Li, Jiayi Chen, Tamanna S. Kumavat, Kyriakos Flouris,
• Abstract summary: We propose QSC-Diffusion, the first fully quantum diffusion-based framework for image generation.<n>We employ parameterized quantum circuits with measurement-induced collapse for reverse denoising.<n>Remarkably, QSC-Diffusion achieves competitive FID scores across multiple datasets.
• Score: 5.258882634977828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing, with its promise of exponential speedups, is rapidly emerging as a powerful paradigm for advancing artificial intelligence. We propose QSC-Diffusion, the first fully quantum diffusion-based framework for image generation. Our method integrates classical Gaussian noise with quantum scrambling in the forward process, and employs parameterized quantum circuits with measurement-induced collapse for reverse denoising -- enabling end-to-end sampling without reliance on classical neural architectures or preprocessing modules. To address optimization challenges in deep quantum models, we introduce a hybrid loss that balances fidelity and diversity, coupled with a divide-and-conquer training strategy to mitigate barren plateaus. Remarkably, QSC-Diffusion achieves competitive FID scores across multiple datasets while using orders of magnitude fewer parameters, outperforming even some quantum-classical hybrid baselines in efficiency. These results highlight the potential of quantum-native generative modeling and mark a foundational step toward scalable quantum machine learning.

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