Charged Particle Tracking with Quantum AnnealingInspired Optimization
Abstract
At the High Luminosity Large Hadron Collider (HLLHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling 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 DenbyPeterson (Hopfield) network method to the quantum annealing framework and to HLLHC conditions. Furthermore, we develop additional techniques to embed the problem onto existing and nearterm quantum annealing hardware. Results using simulated annealing and quantum annealing with the DWave 2X system on the TrackML dataset are presented, demonstrating the successful application of a quantum annealinginspired algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardwarespecific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking.
 Authors:

 Caltech, IQI
 Harvard U.
 UC, San Diego
 Lockheed Martin, Moorestown
 Southern California U.
 Publication Date:
 Research Org.:
 Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), High Energy Physics (HEP) (SC25)
 OSTI Identifier:
 1599326
 Report Number(s):
 arXiv:1908.04475; FERMILABPUB19670PPD
oai:inspirehep.net:1749369
 DOE Contract Number:
 AC0207CH11359
 Resource Type:
 Journal Article
 Journal Name:
 TBD
 Additional Journal Information:
 Journal Name: TBD
 Country of Publication:
 United States
 Language:
 English
 Subject:
 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Zlokapa, Alexander, Anand, Abhishek, Vlimant, JeanRoch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, and Spiropulu, Maria. Charged Particle Tracking with Quantum AnnealingInspired Optimization. United States: N. p., 2019.
Web.
Zlokapa, Alexander, Anand, Abhishek, Vlimant, JeanRoch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, & Spiropulu, Maria. Charged Particle Tracking with Quantum AnnealingInspired Optimization. United States.
Zlokapa, Alexander, Anand, Abhishek, Vlimant, JeanRoch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, and Spiropulu, Maria. Mon .
"Charged Particle Tracking with Quantum AnnealingInspired Optimization". United States. https://www.osti.gov/servlets/purl/1599326.
@article{osti_1599326,
title = {Charged Particle Tracking with Quantum AnnealingInspired Optimization},
author = {Zlokapa, Alexander and Anand, Abhishek and Vlimant, JeanRoch and Duarte, Javier M. and Job, Joshua and Lidar, Daniel and Spiropulu, Maria},
abstractNote = {At the High Luminosity Large Hadron Collider (HLLHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling 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 DenbyPeterson (Hopfield) network method to the quantum annealing framework and to HLLHC conditions. Furthermore, we develop additional techniques to embed the problem onto existing and nearterm quantum annealing hardware. Results using simulated annealing and quantum annealing with the DWave 2X system on the TrackML dataset are presented, demonstrating the successful application of a quantum annealinginspired algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardwarespecific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking.},
doi = {},
journal = {TBD},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}