Experimental quantum adversarial learning with programmable superconducting qubits. (arXiv:2204.01738v1 [quant-ph])

Quantum computing promises to enhance machine learning and artificial
intelligence. Different quantum algorithms have been proposed to improve a wide
spectrum of machine learning tasks. Yet, recent theoretical works show that,
similar to traditional classifiers based on deep classical neural networks,
quantum classifiers would suffer from the vulnerability problem: adding tiny
carefully-crafted perturbations to the legitimate original data samples would
facilitate incorrect predictions at a notably high confidence level. This will
pose serious problems for future quantum machine learning applications …

arxiv experimental learning quantum qubits