Optimal provable robustness of quantum classification via quantum hypothesis testing
Abstract 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 RPM Sensor classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems.These can arise