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Embedding of particle tracking data using hybrid quantum-classical neural networks

Rieger, Carla and Tüysüz, Cenk and Novotny, Kristiane and Vallecorsa, Sofia and Demirköz, Bilge and Potamianos, Karolos and Dobos, Daniel and Vlimant, Jean-Roch (2021) Embedding of particle tracking data using hybrid quantum-classical neural networks. EPJ Web of Conferences, 251 . Art. No. 03065. ISSN 2100-014X. doi:10.1051/epjconf/202125103065. https://resolver.caltech.edu/CaltechAUTHORS:20210930-165301080

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Abstract

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results.


Item Type:Article
Related URLs:
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https://doi.org/10.1051/epjconf/202125103065DOIArticle
ORCID:
AuthorORCID
Vlimant, Jean-Roch0000-0002-9705-101X
Additional Information:© The Authors, published by EDP Sciences, 2021. 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: 23 August 2021. Part of this work was conducted at QUTE, the CTIC quantum computing simulation platform. We thank Luis Meijueiro from the QUTE team at CTIC as well as Alessandro Roggero for fruitful discussions.
DOI:10.1051/epjconf/202125103065
Record Number:CaltechAUTHORS:20210930-165301080
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210930-165301080
Official Citation:Embedding of particle tracking data using hybrid quantum-classical neural networks. Carla Rieger, Cenk Tüysüz, Kristiane Novotny, Sofia Vallecorsa, Bilge Demirköz, Karolos Potamianos, Daniel Dobos and Jean-Roch Vlimant. EPJ Web Conf., 251 (2021) 03065; DOI: https://doi.org/10.1051/epjconf/202125103065
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111117
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Oct 2021 20:26
Last Modified:04 Oct 2021 20:26

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