A purely Quantum Generative Modeling through Unitary Scrambling and Collapse

Yihua Li, Jiayi Chen, Tamanna Surendar Kumavat, Kyriakos Flouris

Keywords: Quantum Machine Learning, Generative modeling

Abstract: Quantum computing offers fundamentally more expressive mechanisms for generative modeling, yet current approaches remain constrained by classical neural components that bottleneck quantum capability and hardware efficiency. We propose the \ac{qsc}, a purely quantum paradigm that eliminates classical architectural dependencies. QGen implements two coherent processes: scrambling, which interleaves Gaussian diffusion channels with unitary delocalization to disperse information globally while avoiding collapse into uninformative states; and collapse, where parameterized quantum circuits refocus scrambled distributions into structured outputs, achieving distributional reconstruction under coherent evolution. To enable scalability, we introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus. Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling, demonstrating strong feasibility for near-term hardware.

Primary Area: generative models

Submission Number: 11751