Lipschitz-regularized training of variational quantum models reduces the generalization gap and improves robustness on a chaotic time-series inference task, with trainable data encoding outperforming fixed encoding.
Provable defenses against adversarial examples via the convex outer adversarial polytope,
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The interplay of robustness and generalization in quantum machine learning
Lipschitz-regularized training of variational quantum models reduces the generalization gap and improves robustness on a chaotic time-series inference task, with trainable data encoding outperforming fixed encoding.