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Title: Charged Particle Tracking with Quantum Annealing-Inspired Optimization

Abstract

At the High Luminosity Large Hadron Collider (HL-LHC), 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 Denby-Peterson (Hopfield) network method to the quantum annealing framework and to HL-LHC conditions. Furthermore, 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 dataset are presented, demonstrating the successful application of a quantum annealing-inspired 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 LHC while leaving open the possibility of a quantum speedup for tracking.

Authors:
 [1];  [2];  [1]; ORCiD logo [3];  [4];  [5];  [1]
  1. Caltech, IQI
  2. Harvard U.
  3. UC, San Diego
  4. Lockheed Martin, Moorestown
  5. 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) (SC-25)
OSTI Identifier:
1599326
Report Number(s):
arXiv:1908.04475; FERMILAB-PUB-19-670-PPD
oai:inspirehep.net:1749369
DOE Contract Number:  
AC02-07CH11359
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, Jean-Roch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, and Spiropulu, Maria. Charged Particle Tracking with Quantum Annealing-Inspired Optimization. United States: N. p., 2019. Web.
Zlokapa, Alexander, Anand, Abhishek, Vlimant, Jean-Roch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, & Spiropulu, Maria. Charged Particle Tracking with Quantum Annealing-Inspired Optimization. United States.
Zlokapa, Alexander, Anand, Abhishek, Vlimant, Jean-Roch, Duarte, Javier M., Job, Joshua, Lidar, Daniel, and Spiropulu, Maria. Mon . "Charged Particle Tracking with Quantum Annealing-Inspired Optimization". United States. https://www.osti.gov/servlets/purl/1599326.
@article{osti_1599326,
title = {Charged Particle Tracking with Quantum Annealing-Inspired 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 = {At the High Luminosity Large Hadron Collider (HL-LHC), 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 Denby-Peterson (Hopfield) network method to the quantum annealing framework and to HL-LHC conditions. Furthermore, 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 dataset are presented, demonstrating the successful application of a quantum annealing-inspired 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 LHC while leaving open the possibility of a quantum speedup for tracking.},
doi = {},
journal = {TBD},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}