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Title: A Pattern Recognition Algorithm for Quantum Annealers

Abstract

Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.

Authors:
ORCiD logo; ; ; ORCiD logo; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1619431
Alternate Identifier(s):
OSTI ID: 1765577
Grant/Contract Number:  
AC02-05CH11231; KA2401032
Resource Type:
Published Article
Journal Name:
Computing and Software for Big Science
Additional Journal Information:
Journal Name: Computing and Software for Big Science Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2510-2036
Publisher:
Springer
Country of Publication:
Germany
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Quantum annealing; Pattern recognition; HEP particle tracking

Citation Formats

Bapst, Frédéric, Bhimji, Wahid, Calafiura, Paolo, Gray, Heather, Lavrijsen, Wim, Linder, Lucy, and Smith, Alex. A Pattern Recognition Algorithm for Quantum Annealers. Germany: N. p., 2019. Web. https://doi.org/10.1007/s41781-019-0032-5.
Bapst, Frédéric, Bhimji, Wahid, Calafiura, Paolo, Gray, Heather, Lavrijsen, Wim, Linder, Lucy, & Smith, Alex. A Pattern Recognition Algorithm for Quantum Annealers. Germany. https://doi.org/10.1007/s41781-019-0032-5
Bapst, Frédéric, Bhimji, Wahid, Calafiura, Paolo, Gray, Heather, Lavrijsen, Wim, Linder, Lucy, and Smith, Alex. Mon . "A Pattern Recognition Algorithm for Quantum Annealers". Germany. https://doi.org/10.1007/s41781-019-0032-5.
@article{osti_1619431,
title = {A Pattern Recognition Algorithm for Quantum Annealers},
author = {Bapst, Frédéric and Bhimji, Wahid and Calafiura, Paolo and Gray, Heather and Lavrijsen, Wim and Linder, Lucy and Smith, Alex},
abstractNote = {Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.},
doi = {10.1007/s41781-019-0032-5},
journal = {Computing and Software for Big Science},
number = 1,
volume = 4,
place = {Germany},
year = {2019},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1007/s41781-019-0032-5

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