CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction

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10.5281zenodo.4034371.pdf

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2020-4-20

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Tüysüz, Cenk
Demirköz, Melahat Bilge
Dobos, Daniel
Fracas, Fabio
Carminati, Federico
Vlimant, Jean-Roch
Potamianos, Karolos
Novotny, Kristiane
Vallecorsa, Sofia

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The unprecedented increase of complexity and scale of data is expected in the necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, processing events as a graph connecting track measurements, are a promising solution and can reduce the combinatorial background to a manageable amount and are scaling to a computationally reasonable size. In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search in a large parameter space. As the next step in this paper, we present an improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model.

URI

https://hdl.handle.net/11511/68893

DOI

https://doi.org/10.5281/zenodo.4034371

Conference Name

Connecting the Dots Workshop (2020)- (CTD2020)

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Department of Physics, Conference / Seminar

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CTD2020: Exploring (Quantum) Track Reconstruction Algorithms for non-HEP applications Novotny, Kristiane; Dobos, Daniel; Demirköz, Melahat Bilge; Tüysüz, Cenk; Fracas, Fabio; Carminati, Federico; Vlimant, Jean-Roch; Potamianos, Karolos; Vallecorsa, Sofia (2020-4-20) The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. High occupancy, track density, complexity and fast growth therefore exponentially increase the demand of algorithms in terms of time, memory and computing resources. While traditionally Kalman filter (or even simpler algorithms)...
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Citation Formats

C. Tüysüz et al., “CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction,” 2020, p. 1, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68893.