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Robust quantum classifiers via NISQ adversarial learning

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The vulnerability of quantum machine learning is demonstrated on a superconducting quantum computer, together with a defense strategy based on noisy intermediate-scale quantum (NISQ) adversarial learning.

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Fig. 1: Adversarial attack on a quantum classifier.

References

  1. Biamonte, J. et al. Nature 549, 195–202 (2017).

    Article  Google Scholar 

  2. Gebhart, V. et al. Preprint at https://arxiv.org/abs/2207.00298 (2022).

  3. Ren, W. et al., https://doi.org/10.1038/s43588-022-00351-9 (2022).

  4. Preskill, J. Quantum 2, 79 (2018).

    Article  Google Scholar 

  5. Liu, Y., Arunachalam, S. & Temme, K. Nat. Phys. 17, 1013–1017 (2021).

    Article  Google Scholar 

  6. Huang, H.-Y. et al. Science 376, 1182–1186 (2022).

    Article  MathSciNet  Google Scholar 

  7. Sharif, M., Bhagavatula, S., Bauer, L. & Reiter, M. K. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security 1528–1540 (Association for Computing Machinery, 2016).

  8. Wu, D., Xia, S.-T. & Wang, Y. Adv. Neural Inf. Process. Syst. 33, 2958–2969 (2020).

    Google Scholar 

  9. Banchi, L., Pereira, J. & Pirandola, S. PRX Quantum 2, 040321 (2021).

    Article  Google Scholar 

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Correspondence to Leonardo Banchi.

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Banchi, L. Robust quantum classifiers via NISQ adversarial learning. Nat Comput Sci 2, 699–700 (2022). https://doi.org/10.1038/s43588-022-00359-1

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