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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.

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Acknowledgements

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.

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Correspondence to Sau Lan Wu.

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The authors declare no competing interests.

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Related links

Google quantum computing journey: https://quantumai.google/learn/map

IBM’s roadmap for scaling quantum technology: https://research.ibm.com/blog/ibm-quantum-roadmap

IonQ’s Roadmap up to 2025: https://ionq.com/posts/december-09-2020-scaling-quantum-computer-roadmap

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Wu, S.L., Yoo, S. Challenges and opportunities in quantum machine learning for high-energy physics. Nat Rev Phys 4, 143–144 (2022). https://doi.org/10.1038/s42254-022-00425-7

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