July 28, 2020 to August 6, 2020
virtual conference
Europe/Prague timezone

Quantum Track Reconstruction Algorithms for non-HEP applications

Jul 29, 2020, 4:30 PM
virtual conference

virtual conference

Talk 17. Technology Applications, Industrial Opportunities and Sustainability Technology Applications, Industrial Opportunities and Sustainability


Kristiane Sylvia Novotny (Johannes-Gutenberg-Universitaet Mainz (DE))


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.

Primary authors

Daniel Dobos (Lancaster University (GB)) Kristiane Sylvia Novotny (Johannes-Gutenberg-Universitaet Mainz (DE)) Cenk Tuysuz (Middle East Technical University (TR)) Bilge Demirkoz (Middle East Technical University (TR)) Karolos Potamianos (Deutsches Elektronen-Synchrotron (DE))

Presentation materials