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

doi:10.1109/ICASSP49357.2023.10095030

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

^*Corresponding author for this work

National Taiwan University
National Yang Ming Chiao Tung University
Georgia Institute of Technology
IBM

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

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.

Original language	English
Title of host publication	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728163277
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10095030
State	Published - 2023
Externally published	Yes
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 04 06 2023 → 10 06 2023

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2023-June
ISSN (Print)	1520-6149

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	04/06/23 → 10/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/ICASSP49357.2023.10095030

Cite this

Huang, J. C., Tsai, Y. L., Yang, C. H. H., Su, C. F., Yu, C. M., Chen, P. Y., & Kuo, S. Y. (2023). Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP49357.2023.10095030

Huang, Jhih Cing ; Tsai, Yu Lin ; Yang, Chao Han Huck et al. / Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{3495228f7f2c4026840e8acbb332d8ca,

title = "Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise",

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.",

author = "Huang, \{Jhih Cing\} and Tsai, \{Yu Lin\} and Yang, \{Chao Han Huck\} and Su, \{Cheng Fang\} and Yu, \{Chia Mu\} and Chen, \{Pin Yu\} and Kuo, \{Sy Yen\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

year = "2023",

doi = "10.1109/ICASSP49357.2023.10095030",

language = "英语",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",

address = "美国",

}

Huang, JC, Tsai, YL, Yang, CHH, Su, CF, Yu, CM, Chen, PY & Kuo, SY 2023, Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, 04/06/23. https://doi.org/10.1109/ICASSP49357.2023.10095030

Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise. / Huang, Jhih Cing; Tsai, Yu Lin; Yang, Chao Han Huck et al.
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise

AU - Huang, Jhih Cing

AU - Tsai, Yu Lin

AU - Yang, Chao Han Huck

AU - Su, Cheng Fang

AU - Yu, Chia Mu

AU - Chen, Pin Yu

AU - Kuo, Sy Yen

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85177570431

U2 - 10.1109/ICASSP49357.2023.10095030

DO - 10.1109/ICASSP49357.2023.10095030

M3 - 会议稿件

AN - SCOPUS:85177570431

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

Y2 - 4 June 2023 through 10 June 2023

ER -

Huang JC, Tsai YL, Yang CHH, Su CF, Yu CM, Chen PY et al. Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP49357.2023.10095030

Certified Robustness of Quantum Classifiers Against Adversarial Examples Through Quantum Noise

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this