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.
- 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. doi: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}
}
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https://doi.org/10.1007/s41781-019-0032-5
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