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Backdoor attacks against Hybrid Classical-Quantum Neural Networks.

Ji Guo1, Wenbo Jiang2, Rui Zhang2

  • 1Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 29, 2025
PubMed
Summary

This study introduces the first systematic analysis of backdoor attacks on Hybrid Classical-Quantum Neural Networks (HQNNs). Results show HQNNs are more robust against these attacks than traditional Convolutional Neural Networks (CNNs).

Keywords:
Artificial intelligence securityBackdoor attacksHybrid Classical-Quantum Neural NetworksTrustworthy artificial intelligence

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Area of Science:

  • Quantum Machine Learning
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Hybrid Classical-Quantum Neural Networks (HQNNs) are an emerging area in Quantum Machine Learning (QML).
  • The security vulnerabilities, specifically backdoor attacks, of HQNNs remain largely unexplored.
  • Understanding these vulnerabilities is crucial for the secure deployment of QML technologies.

Purpose of the Study:

  • To conduct the first systematic investigation into backdoor attacks targeting HQNNs.
  • To analyze the theoretical underpinnings of backdoor attacks on HQNNs, including generalization bounds and perturbation requirements.
  • To compare the robustness of HQNNs against backdoor attacks with that of conventional Convolutional Neural Networks (CNNs).

Main Methods:

  • Development of a novel framework for backdoor attacks on HQNNs.
  • Theoretical analysis of generalization bounds and minimum perturbation for HQNN backdoor attacks.
  • Implementation and evaluation of two established backdoor attack methods on both HQNNs and CNNs.
  • Introduction of the 'Qcolor' backdoor attack utilizing color shifts as triggers and NSGA-II for hyperparameter optimization.

Main Results:

  • HQNNs exhibit greater robustness against backdoor attacks compared to CNNs.
  • Successful backdoor attacks on HQNNs require more substantial image perturbations than on CNNs.
  • The proposed Qcolor backdoor attack demonstrates effectiveness, stealthiness, and robustness, validated through extensive experimentation.
  • NSGA-II effectively optimized hyperparameters for the Qcolor backdoor attack.

Conclusions:

  • HQNNs offer enhanced resilience against certain types of backdoor attacks compared to current deep learning models.
  • The Qcolor backdoor presents a novel and potent threat vector in the context of QML security.
  • Further research is warranted to develop robust defense mechanisms against sophisticated backdoor attacks on HQNNs.