Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise
Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang, Cheng-Fang Su, Chia-Mu Yu, Pin-Yu Chen, Sy-Yen Kuo
Abstract
Recently, quantum classifiers have been known to be vulnerable to adversarial attacks, where quantum classifiers are fooled by imperceptible noises to have misclassification. In this paper, we propose one first theoretical study that utilizing the added quantum random rotation noise can improve the robustness of quantum classifiers against adversarial attacks. We connect the definition of differential privacy and demonstrate the quantum classifier trained with the natural presence of additive noise is differentially private. Lastly, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples supported by experimental results.
BibTeX
@inproceedings{icassp2023_certifiedrobustn,
title = {Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise},
author = {Jhih-Cing Huang and Yu-Lin Tsai and Chao-Han Huck Yang and Cheng-Fang Su and Chia-Mu Yu and Pin-Yu Chen and Sy-Yen Kuo},
booktitle = {ICASSP 2023},
year = {2023}
}