A proof that structured projection followed by rank-revealing orthonormalization always recovers the unitary eigenbasis and eigenvalues with correct multiplicities from the real embedding.
Experimental realization of any discrete unitary operato r
4 Pith papers cite this work, alongside 1,784 external citations. Polarity classification is still indexing.
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Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
In finite-depth random linear optical circuits, entanglement grows at most diffusively and robust circuit complexity scales similarly, with depth bounds ensuring near-maximal subsystem entanglement and closeness to Haar unitaries.
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Recovering Complex Unitary Eigenspaces from Real-Valued Embeddings
A proof that structured projection followed by rank-revealing orthonormalization always recovers the unitary eigenbasis and eigenvalues with correct multiplicities from the real embedding.
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Extraction of linearized models from pre-trained networks via knowledge distillation
Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
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Entanglement and circuit complexity in finite-depth random linear optical networks
In finite-depth random linear optical circuits, entanglement grows at most diffusively and robust circuit complexity scales similarly, with depth bounds ensuring near-maximal subsystem entanglement and closeness to Haar unitaries.
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