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

Exploring (Quantum) Track Reconstruction Algorithms for non-HEP applications

Apr 21, 2020, 3:35 PM
Virtual/Digital only workshop

Virtual/Digital only workshop


Mrs Kristiane Novotny (GluoNNet) Kristiane Novotny


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) are used, they are expected to scale worse than quadratically and thus strongly increasing the total processing time. Graph Neural Networks (GNN) are currently explored for HEP, but also non HEP trajectory reconstruction applications. Quantum Computers with their feature of evaluating a very large number of states simultaneously are therefore good candidates for such complex searches in large parameter and graph spaces.
In this paper we present our work on implementing a quantum-based graph tracking machine learning algorithm to evaluate Traffic collision avoidance system (TCAS) probabilities of commercial flights.

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

Primary authors

Daniel Dobos (University of Lancaster & gluoNNet) Bilge Demirkoz (Middle East Technical University (METU)) Cenk Tuysuz (Middle East Technical University (TR)) Fabio Fracas (Universita e INFN, Padova (IT)) Federico Carminati (CERN) Jean-Roch Vlimant (California Institute of Technology) Karolos Potamianos (DESY & gluoNNet) Mrs Kristiane Novotny (GluoNNet) Sofia Vallecorsa (CERN)

Presentation materials

Peer reviewing