  Recently, quantum classifiers have been found to be vulnerable to adversarial
attacks, in which quantum classifiers are deceived by imperceptible noises,
leading to misclassification. In this paper, we propose the first theoretical
study demonstrating that adding quantum random rotation noise can improve
robustness in quantum classifiers against adversarial attacks. We link the
definition of differential privacy and show that the quantum classifier trained
with the natural presence of additive noise is differentially private. Finally,
we derive a certified robustness bound to enable quantum classifiers to defend
against adversarial examples, supported by experimental results simulated with
noises from IBM's 7-qubits device.
