eprintid: 10192471
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/24/71
datestamp: 2024-05-17 10:04:17
lastmod: 2024-05-17 10:04:17
status_changed: 2024-05-17 10:04:17
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Duckett, Philippa
creators_name: Facini, Gabriel
creators_name: Jastrzebski, Marcin
creators_name: Malik, Sarah
creators_name: Scanlon, Tim
creators_name: Rettie, Sebastien
title: Reconstructing charged particle track segments with a quantum-enhanced support vector machine
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F60
keywords: Science & Technology, Physical Sciences, Astronomy & Astrophysics, Physics, Particles & Fields, Physics
note: Published by the American Physical Society under the terms of
the Creative Commons Attribution 4.0 International license.
Further distribution of this work must maintain attribution to
the author(s) and the published article’s title, journal citation,
and DOI. Funded by SCOAP3
.
abstract: Reconstructing the trajectories of charged particles from the collection of hits they leave in the detectors of collider experiments like those at the Large Hadron Collider (LHC) is a challenging combinatorics problem and computationally intensive. The tenfold increase in the delivered luminosity at the upgraded High Luminosity LHC will result in a very densely populated detector environment. The time taken by conventional techniques for reconstructing particle tracks scales worse than quadratically with track density. Accurately and efficiently assigning the collection of hits left in the tracking detector to the correct particle will be a computational bottleneck and has motivated studying possible alternative approaches. This paper presents a quantum-enhanced machine learning algorithm that uses a support vector machine (SVM) with a quantum-estimated kernel to classify a set of three hits (triplets) as either belonging to or not belonging to the same particle track. The performance of the algorithm is then compared to a fully classical SVM. The quantum algorithm shows an improvement in accuracy versus the classical algorithm. Model complexity metrics are used to hint at an explanation for favorable performance of the quantum kernel.
date: 2024-03-08
date_type: published
publisher: AMER PHYSICAL SOC
official_url: http://dx.doi.org/10.1103/physrevd.109.052002
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2260995
doi: 10.1103/PhysRevD.109.052002
lyricists_name: Rettie, Sebastien
lyricists_name: Facini, Gabriel
lyricists_id: SRETT26
lyricists_id: GFACI32
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
funding_acknowledgements: [Royal Society]; [Banting Postdoctoral Fellowship program]; [Natural Sciences and Engineering Research Council of Canada]; [STFC]
full_text_status: public
publication: Physical Review D
volume: 109
number: 5
article_number: 052002
pages: 11
issn: 2470-0010
citation:        Duckett, Philippa;    Facini, Gabriel;    Jastrzebski, Marcin;    Malik, Sarah;    Scanlon, Tim;    Rettie, Sebastien;      (2024)    Reconstructing charged particle track segments with a quantum-enhanced support vector machine.                   Physical Review D , 109  (5)    , Article 052002.  10.1103/PhysRevD.109.052002 <https://doi.org/10.1103/PhysRevD.109.052002>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10192471/1/PhysRevD.109.052002.pdf