Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing

Understand

Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts.

• However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems.
• These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks.
• In order to develop defence mechanisms and to better understand the reliability of these algorithms, it is crucial to understand their robustness properties in presence of natural noise sources or adversarial manipulation.