Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Quantum information

Robust quantum classifiers via NISQ adversarial learning

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leonardo Banchi.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-022-00359-1

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics