Entanglement raises the Fisher effective dimension of parameterized quantum circuits, producing a PAC-Bayes generalization bound that correctly ranks circuits of identical parameter count by their train-test gap.
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QFM learns multi-qubit quantum distributions via spin Wigner function representation and functional flow matching, with validation on trace, purity, and entanglement entropy of generated states.
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Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
Entanglement raises the Fisher effective dimension of parameterized quantum circuits, producing a PAC-Bayes generalization bound that correctly ranks circuits of identical parameter count by their train-test gap.
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Generative Modeling of Quantum Distribution with Functional Flow Matching
QFM learns multi-qubit quantum distributions via spin Wigner function representation and functional flow matching, with validation on trace, purity, and entanglement entropy of generated states.