Experimental quantum adversarial learning with programmable superconducting qubits

Understand

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.