QIBP adapts interval bound propagation to quantum neural networks for certified adversarial robustness via interval and affine arithmetic implementations.
An introduction to quantum machine learning
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
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.
A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.
citing papers explorer
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Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
QIBP adapts interval bound propagation to quantum neural networks for certified adversarial robustness via interval and affine arithmetic implementations.
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Efficient Mutation Testing of Quantum Machine Learning Models
New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
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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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Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.