SSDMs introduce an intrinsic score-based diffusion framework on the Fubini-Study manifold to sample quantum pure-state ensembles without classical re-preparation.
International Conference on Learning Representations , year=
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A single-objective rectified flow variant uses neural ODEs trained by regression to monotonically decrease a fixed convex transport cost while preserving marginal distributions.
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Stochastic Schr\"odinger Diffusion Models for Pure-State Ensemble Generation
SSDMs introduce an intrinsic score-based diffusion framework on the Fubini-Study manifold to sample quantum pure-state ensembles without classical re-preparation.
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Rectified Flow: A Marginal Preserving Approach to Optimal Transport
A single-objective rectified flow variant uses neural ODEs trained by regression to monotonically decrease a fixed convex transport cost while preserving marginal distributions.