Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

• URL: http://arxiv.org/abs/2211.00887v2
• Date: Fri, 28 Apr 2023 05:32:38 GMT
• Title: Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise
• Authors: Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang, Cheng-Fang Su, Chia-Mu Yu, Pin-Yu Chen, Sy-Yen Kuo
• Abstract summary: We show that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks. We derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples.
• Score: 68.1992787416233
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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