arXiv:2605.03573v3 Announce Type: replace-cross Abstract: Quantum machine learning increasingly relies on pure-state representations, motivating generative models that sample directly in quantum representation space rather than perturbing classical inputs and re-encoding. We introduce Stochastic Schr\"odinger Diffusion Models (SSDMs), a score-based generative framework that defines diffusion, scores, and reverse-time sampling intrinsically on the complex projective manifold $\mathbb{CP}^{d-1}$ under the Fubini--Study metric. SSDMs combine a Riemannian Ornstein--Uhlenbeck forward diffusion with
The increasing focus on quantum machine learning necessitates direct pure-state generation methods, moving beyond classical input perturbations.
This development could accelerate quantum AI model training and capabilities, fundamentally altering how quantum systems are simulated and engineered.
Generative models can now operate intrinsically within quantum representation space, potentially creating more efficient and accurate quantum AI applications.
- · Quantum computing researchers
- · Quantum AI developers
- · Deep tech VCs
- · Classical generative model developers in quantum contexts
Improved efficiency and accuracy in quantum machine learning model training.
Faster development and deployment of quantum algorithms for complex problem-solving.
Potential for new quantum materials discovery and drug design through advanced simulation capabilities.
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Read at arXiv cs.LG