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Title: Charged particle tracking with quantum annealing optimization

Abstract

Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

Authors:
ORCiD logo; ; ; ; ; ;
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1828631
Alternate Identifier(s):
OSTI ID: 1599326
Report Number(s):
FERMILAB-PUB-19-670-PPD; arXiv:1908.04475
Journal ID: ISSN 2524-4906; 27; PII: 54
Grant/Contract Number:  
AC02-07CH11359
Resource Type:
Published Article
Journal Name:
Quantum Machine Intelligence
Additional Journal Information:
Journal Name: Quantum Machine Intelligence Journal Volume: 3 Journal Issue: 2; Journal ID: ISSN 2524-4906
Publisher:
Springer Science + Business Media
Country of Publication:
Switzerland
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Quantum annealing; High energy physics; Optimization; Pattern recognition; Adiabatic quantum computing

Citation Formats

Zlokapa, Alexander, Anand, Abhishek, Vlimant, Jean-Roch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, and Spiropulu, Maria. Charged particle tracking with quantum annealing optimization. Switzerland: N. p., 2021. Web. doi:10.1007/s42484-021-00054-w.
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Zlokapa, Alexander, Anand, Abhishek, Vlimant, Jean-Roch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, & Spiropulu, Maria. Charged particle tracking with quantum annealing optimization. Switzerland. https://doi.org/10.1007/s42484-021-00054-w
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Zlokapa, Alexander, Anand, Abhishek, Vlimant, Jean-Roch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, and Spiropulu, Maria. Tue . "Charged particle tracking with quantum annealing optimization". Switzerland. https://doi.org/10.1007/s42484-021-00054-w.
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@article{osti_1828631,
title = {Charged particle tracking with quantum annealing optimization},
author = {Zlokapa, Alexander and Anand, Abhishek and Vlimant, Jean-Roch and Duarte, Javier M. and Job, Joshua and Lidar, Daniel and Spiropulu, Maria},
abstractNote = {Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.},
doi = {10.1007/s42484-021-00054-w},
journal = {Quantum Machine Intelligence},
number = 2,
volume = 3,
place = {Switzerland},
year = {Tue Nov 02 00:00:00 EDT 2021},
month = {Tue Nov 02 00:00:00 EDT 2021}
}
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Publisher's Version of Record
https://doi.org/10.1007/s42484-021-00054-w
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