SSDMs introduce an intrinsic score-based diffusion framework on the Fubini-Study manifold to sample quantum pure-state ensembles without classical re-preparation.
G., Onodera, T., Stein, M
3 Pith papers cite this work. Polarity classification is still indexing.
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A photonic CNN performs MNIST classification entirely in the optical domain at 94% accuracy using MZI meshes and microring nonlinearities, with hybrid ex-situ digital twin training followed by in-situ SPSA fine-tuning.
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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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Photonic convolutional neural network with pre-trained in-situ training
A photonic CNN performs MNIST classification entirely in the optical domain at 94% accuracy using MZI meshes and microring nonlinearities, with hybrid ex-situ digital twin training followed by in-situ SPSA fine-tuning.
- Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing