Quantum Adversarial Learning for Kernel Methods (2404.05824v1)
Abstract: We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defence strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks. Our results find applications in security-critical learning problems and in mitigating the effect of some forms of quantum noise, since the attacker can also be understood as part of the surrounding environment.
- B. Biggio and F. Roli, Wild patterns: Ten years after the rise of adversarial machine learning, in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018) pp. 2154–2156.
- M. Schuld, I. Sinayskiy, and F. Petruccione, An introduction to quantum machine learning, Contemporary Physics 56, 172 (2015).
- S. Lu, L.-M. Duan, and D.-L. Deng, Quantum adversarial machine learning, Physical Review Research 2, 033212 (2020).
- N. Liu and P. Wittek, Vulnerability of quantum classification to adversarial perturbations, Physical Review A 101, 062331 (2020).
- Qiskit contributors, Qiskit: An open-source framework for quantum computing (2023).
- M. Schuld and F. Petruccione, Machine learning with quantum computers (Springer, 2021).
- N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods (Cambridge university press, 2000).
- Y. Liu, S. Arunachalam, and K. Temme, A rigorous and robust quantum speed-up in supervised machine learning, Nature Physics 17, 1013 (2021).
- C.-C. Chang and C.-J. Lin, Libsvm: a library for support vector machines, ACM transactions on intelligent systems and technology (TIST) 2, 1 (2011).
- S. Thanasilp, S. Wang, and Z. Holmes, Exponential concentration and untrainability in quantum kernel methods, arXiv preprint arXiv:2208.11060 (2022).
- L. Banchi and G. E. Crooks, Measuring analytic gradients of general quantum evolution with the stochastic parameter shift rule, Quantum 5, 386 (2021).
- L. Banchi, Robust quantum classifiers via nisq adversarial learning, Nature Computational Science 2, 699 (2022).
- L. Banchi, J. Pereira, and S. Pirandola, Generalization in quantum machine learning: A quantum information standpoint, PRX Quantum 2, 040321 (2021).
- P. Georgiou, S. T. Jose, and O. Simeone, Adversarial quantum machine learning: An information-theoretic generalization analysis, arXiv preprint arXiv:2402.00176 (2024).
- T. Wang, D. Zhao, and S. Tian, An overview of kernel alignment and its applications, Artificial Intelligence Review 43, 179 (2015).
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.