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To this end, the following proposition upper bounds the scaled excess RCSC r,p,ϵ for the quantum embeddings introduced in Section II A

Impact of Quantum Embeddings We now discuss some of the insights gained from the upper bound in Theorem 3 about the choice of quantum embedding on adversarial generalization

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On the Generalization of Adversarially Trained Quantum Classifiers

quant-ph · 2025-04-24 · conditional · novelty 6.0

For adversarially trained quantum classifiers, the excess sample complexity over standard training vanishes with input dimension for rotation embeddings under classical attacks, scales at least linearly for amplitude embeddings, and depends only on Hilbert space dimension under quantum attacks.

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  • On the Generalization of Adversarially Trained Quantum Classifiers quant-ph · 2025-04-24 · conditional · none · ref 1

    For adversarially trained quantum classifiers, the excess sample complexity over standard training vanishes with input dimension for rotation embeddings under classical attacks, scales at least linearly for amplitude embeddings, and depends only on Hilbert space dimension under quantum attacks.