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.
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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.
| Type: | Article |
|---|---|
| Title: | Reconstructing charged particle track segments with a quantum-enhanced support vector machine |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1103/PhysRevD.109.052002 |
| Publisher version: | http://dx.doi.org/10.1103/physrevd.109.052002 |
| Language: | English |
| Additional information: | 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 . |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, Physics, Particles & Fields, Physics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10192471 |
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