Quantum neural estimators achieve minimax-optimal copy complexity O(|Θ(U)| d / ε²) with sub-Gaussian concentration for measured Rényi relative entropies on density pairs with bounded Thompson metric.
Quantum machine learning
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UNVERDICTED 3representative citing papers
A quantum autoencoder purifies adversarial perturbations for quantum classifiers and supplies a confidence score for unrecoverable inputs, claiming up to 68% accuracy gains over prior defenses without adversarial training.
Empirical comparison of angle and amplitude encoding in VQCs on Wine and Diabetes datasets shows rotational gate selection in the encoding layer changes accuracy by 10-41 percent and treats embedding as a tunable hyperparameter.
citing papers explorer
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Performance Guarantees for Quantum Neural Estimation of Entropies
Quantum neural estimators achieve minimax-optimal copy complexity O(|Θ(U)| d / ε²) with sub-Gaussian concentration for measured Rényi relative entropies on density pairs with bounded Thompson metric.
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Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
A quantum autoencoder purifies adversarial perturbations for quantum classifiers and supplies a confidence score for unrecoverable inputs, claiming up to 68% accuracy gains over prior defenses without adversarial training.
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Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy
Empirical comparison of angle and amplitude encoding in VQCs on Wine and Diabetes datasets shows rotational gate selection in the encoding layer changes accuracy by 10-41 percent and treats embedding as a tunable hyperparameter.