Skip to main content

Thank you for visiting 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.

Challenges and opportunities in quantum machine learning for high-energy physics

Quantum machine learning may provide powerful tools for data analysis in high-energy physics. Sau Lan Wu and Shinjae Yoo describe how the potential of these tools is starting to be tested and what has been understood thus far.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. Havlek, V. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019).

    ADS  Article  Google Scholar 

  2. Schuld, M. & Killoran, N. Quantum machine learning in feature Hilbert spaces. Phys. Rev. Lett. 122, 040504 (2019).

    ADS  Article  Google Scholar 

  3. Wu, S. L. et al. Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits. J. Phys. G: Nucl. Part. Phys 48, 125003 (2021).

    ADS  Article  Google Scholar 

  4. Wu, S. L. et al. Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC. Phys. Rev. Res. 3, 033221 (2021).

    Article  Google Scholar 

  5. The DELPHES 3 collaboration. et al. DELPHES 3: a modular framework for fast simulation of a generic collider experiment. J. High Energy Phys. 2014, 57 (2014).

    Google Scholar 

  6. Eddins, A. et al. Doubling the size of quantum simulators by entanglement forging. PRX Quantum 3, 010309 (2022).

  7. Zhukov, A. A. & Pogosov, W. V. Quantum error reduction with deep neural network applied at the post-processing stage. Preprint at (2021).

  8. Maciejewski, F. B., Zimborás, Z. & Oszmanie, M. Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomography. Quantum 4, 257 (2020).

    Article  Google Scholar 

  9. Mott, A. et al. Solving a Higgs optimization problem with quantum annealing for machine learning. Nature 550, 375–379 (2017).

    ADS  Article  Google Scholar 

Download references


This work is supported in part by the United States Department of Energy, Office of Science, High Energy Physics QuantISED Program, under Award Number DE-SC-0020416 and DE-SC-0012704 and by the Vilas foundation at the University of Wisconsin.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Sau Lan Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Related links

Google quantum computing journey:

IBM’s roadmap for scaling quantum technology:

IonQ’s Roadmap up to 2025:

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, S.L., Yoo, S. Challenges and opportunities in quantum machine learning for high-energy physics. Nat Rev Phys 4, 143–144 (2022).

Download citation

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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