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Particle Track Reconstruction with Quantum Algorithms

Tüysüz, Cenk and Carminati, Federico and Demirköz, Bilge and Dobos, Daniel and Fracas, Fabio and Novotny, Kristiane and Potamianos, Karolos and Vallecorsa, Sofia and Vlimant, Jean-Roch (2020) Particle Track Reconstruction with Quantum Algorithms. EPJ Web of Conferences, 245 . Art. No. 09013. ISSN 2100-014X.

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Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical “closeness” of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much “cleaner” initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.

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Vlimant, Jean-Roch0000-0002-9705-101X
Additional Information:© The Authors, published by EDP Sciences, 2020. This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Published online: 16 November 2020. Part of this work was conducted at "iBanks", the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of "iBanks". This work was partially supported by Turkish Atomic Energy Authority (TAEK) (Grant No: 2017TAEKCERN-A5.H6.F2.15). Cenk Tüysüz thanks Oral Okan and Egemen Sert from STB for their valuable discussions.
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SuperMicro CorporationUNSPECIFIED
Kavli FoundationUNSPECIFIED
Türkiye Atom Enerjisi Kurumu (TAEK)2017TAEKCERN-A5.H6.F2.15
Record Number:CaltechAUTHORS:20201117-080030193
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Official Citation:Particle Track Reconstruction with Quantum Algorithms. Cenk Tüysüz, Federico Carminati, Bilge Demirköz, Daniel Dobos, Fabio Fracas, Kristiane Novotny, Karolos Potamianos, Sofia Vallecorsa and Jean-Roch Vlimant. EPJ Web Conf., 245 (2020) 09013; DOI:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106691
Deposited By: Tony Diaz
Deposited On:17 Nov 2020 16:14
Last Modified:17 Nov 2020 16:14

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