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

Journal Article · · Nature Reviews Physics
 [1];  [2]
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
+ Show Author Affiliations

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
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 journal February 2022
Solving a Higgs optimization problem with quantum annealing for machine learning journal October 2017
Supervised learning with quantum-enhanced feature spaces 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 journal July 2021
Doubling the Size of Quantum Simulators by Entanglement Forging journal January 2022
Quantum Machine Learning in Feature Hilbert Spaces journal February 2019
Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC journal September 2021
Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomography journal April 2020

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