Apr 20 – 30, 2020
Virtual/Digital only workshop
Europe/Paris timezone

A Quantum Graph Network Approach to Particle Track Reconstruction

Apr 20, 2020, 2:55 PM
Virtual/Digital only workshop

Virtual/Digital only workshop


Mr Cenk Tuysuz (Middle East Technical University (TR))


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.

Second most appropriate track (if necessary) Enhanced performance of tracking algorithms
Consider for young scientist forum (Student or postdoc speaker) Yes

Primary author

Mr Cenk Tuysuz (Middle East Technical University (TR))


Bilge Demirkoz (Middle East Technical University (TR)) Dr Daniel Dobos (gluoNNet & THE Port) Prof. Fabio Fracas (Universita e INFN, Padova (IT)) Federico Carminati (CERN) Dr Jean-Roch Vlimant (California Institute of Technology (US)) Karolos Potamianos (Deutsches Elektronen-Synchrotron (DE)) Mrs Kristiane Novotny (GluoNNet) Dr Sofia Vallecorsa (CERN)

Presentation materials

Peer reviewing