Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
arXiv:2604.28176v1 Announce Type: cross
Abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can caus…