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This paper evaluates federated learning for privacy-preserving hybrid quantum-classical machine learning, demonstrating improved accuracy over local training while reducing communication and maintaining parameter efficiency.
This paper characterizes the inherent interpretability of linear models vs. single-qubit mixed-state models for binary classification, showing that the quantum model learns a hyperellipsoid instead of a hyperplane, with implications for inductive biases and pedagogy.