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Quantum adversarial machine learning is an emergent interdisciplinary research frontier that studies the vulnerability of quantum learning systems in adversarial scenarios and the development of potential countermeasures to enhance their robustness against adversarial perturbations. In this talk, I will first make a brief introduction to this field and review some recent progress. I will show, through concrete examples, that typical quantum classifiers are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully crafted noises into the original legitimate samples may lead the classifiers to make incorrect predictions at a high confidence level. I will talk about possible defense strategies against adversarial attacks. I will also talk about a recent experimental demonstration of quantum adversarial learning with programmable superconducting qubits.

Reference:
[1] S.-R. Lu, L. M. Duan, and D.-L. Deng, Phys. Rev. Research 2, 033212 (2020)
[2] W.-Y. Gong and D.-L. Deng, National Science Review 9, nwab130 (2022)
[3] W.-H. Ren et al., Nature Computational Science 2, 711 (2022)
[4] H.-L. Zhang et al., Nature Communications 13, 4993 (2022)