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

Wu, Sau Lan; Yoo, Shinjae

doi:10.1038/s42254-022-00425-7

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

Journal Article · Tue Feb 15 00:00:00 EST 2022 · Nature Reviews Physics

DOI:https://doi.org/10.1038/s42254-022-00425-7· OSTI ID:1841105

Wu, Sau Lan ^[1]; Yoo, Shinjae ^[2]

Univ. of Wisconsin, Madison, WI (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)

Quantum machine learning may provide powerful tools for data analysis in high-energy physics. Here, 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.

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), High Energy Physics (HEP); Vilas Foundation

Grant/Contract Number:: SC0012704; SC0020416

OSTI ID:: 1841105

Alternate ID(s):: OSTI ID: 1969353

Report Number(s):: BNL-222602-2022-JAAM; BNL-224165-2023-JAAM

Journal Information:: Nature Reviews Physics, Journal Name: Nature Reviews Physics Journal Issue: 3 Vol. 4; ISSN 2522-5820

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

References (8)

Quantum error reduction with deep neural network applied at the post-processing stage Zhukov, Andrey; Pogosov, Walter Quantum Information Processing, Vol. 21, Issue 3 https://doi.org/10.1007/s11128-022-03433-9	journal	February 2022
Solving a Higgs optimization problem with quantum annealing for machine learning Mott, Alex; Job, Joshua; Vlimant, Jean-Roch Nature, Vol. 550, Issue 7676 https://doi.org/10.1038/nature24047	journal	October 2017
Supervised learning with quantum-enhanced feature spaces Havlíček, Vojtěch; Córcoles, Antonio D.; Temme, Kristan Nature, Vol. 567, Issue 7747 https://doi.org/10.1038/s41586-019-0980-2	journal	March 2019
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 Wu, Sau Lan; Chan, Jay; Guan, Wen Journal of Physics G: Nuclear and Particle Physics https://doi.org/10.1088/1361-6471/ac1391	journal	July 2021
Doubling the Size of Quantum Simulators by Entanglement Forging Eddins, Andrew; Motta, Mario; Gujarati, Tanvi P. PRX Quantum, Vol. 3, Issue 1 https://doi.org/10.1103/PRXQuantum.3.010309	journal	January 2022
Quantum Machine Learning in Feature Hilbert Spaces Schuld, Maria; Killoran, Nathan Physical Review Letters, Vol. 122, Issue 4 https://doi.org/10.1103/PhysRevLett.122.040504	journal	February 2019
Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC Wu, Sau Lan; Sun, Shaojun; Guan, Wen Physical Review Research, Vol. 3, Issue 3 https://doi.org/10.1103/PhysRevResearch.3.033221	journal	September 2021
Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomography Maciejewski, Filip B.; Zimborás, Zoltán; Oszmaniec, Michał Quantum, Vol. 4 https://doi.org/10.22331/q-2020-04-24-257	journal	April 2020

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

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