Quantum masked autoencoders reconstruct masked MNIST-family images in quantum states and achieve 12.86% higher average classification accuracy than prior quantum autoencoders under masking.
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TART improves clean accuracy in adversarial training by modulating perturbation bounds according to the tangential component of adversarial examples.
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
citing papers explorer
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Quantum Masked Autoencoders for Vision Learning
Quantum masked autoencoders reconstruct masked MNIST-family images in quantum states and achieve 12.86% higher average classification accuracy than prior quantum autoencoders under masking.
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Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training
TART improves clean accuracy in adversarial training by modulating perturbation bounds according to the tangential component of adversarial examples.
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Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.