  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 in
safety and security-critical scenarios. Here, we report the first experimental
demonstration of quantum adversarial learning with programmable superconducting
qubits. We train quantum classifiers, which are built upon variational quantum
circuits consisting of ten transmon qubits featuring average lifetimes of 150
$\mu$s, and average fidelities of simultaneous single- and two-qubit gates
above 99.94% and 99.4% respectively, with both real-life images (e.g., medical
magnetic resonance imaging scans) and quantum data. We demonstrate that these
well-trained classifiers (with testing accuracy up to 99%) can be practically
deceived by small adversarial perturbations, whereas an adversarial training
process would significantly enhance their robustness to such perturbations. Our
results reveal experimentally a crucial vulnerability aspect of quantum
learning systems under adversarial scenarios and demonstrate an effective
defense strategy against adversarial attacks, which provide a valuable guide
for quantum artificial intelligence applications with both near-term and future
quantum devices.

Quantum Physics;Disordered Systems and Neural Networks;Artificial Intelligence;Machine Learning