Title: A purely Quantum Generative Modeling through  Unitary Scrambling and Collapse
Primary area: generative models
Keywords: Quantum Machine Learning, Generative modeling

Abstract: Quantum computing, by exploiting superposition and entanglement, offers new opportunities to rethink generative modeling beyond GANs, VAEs, and DDPMs. Yet existing quantum generative approaches continue to depend on classical neural architectures or preprocessing modules and thus fall short of demonstrating fully quantum capability. We introduce QSC-GM, a new quantum-native paradigm for image generation that eliminates classical dependencies. QSC-GM leverages unitary scrambling to distribute information across the Hilbert space and measurement-induced collapse to extract structured samples, defining a novel mechanism for probability transformation. Optimization in deep quantum circuits is notoriously hampered by barren plateaus; we alleviate this issue by formulating intermediate tasks that interpolate between noise and data, enabling stable and efficient training. On benchmark datasets, QSC-GM surpasses classical baselines under equal parameter budgets and achieves competitive performance with hybrid quantum classical models, highlighting quantum native generative modeling as a promising direction for scalable quantum machine learning.