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
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.


CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction
Tüysüz, Cenk; Demirköz, Melahat Bilge; Dobos, Daniel; Fracas, Fabio; Carminati, Federico; Vlimant, Jean-Roch; Potamianos, Karolos; Novotny, Kristiane; Vallecorsa, Sofia (2020-4-20)
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 previ...
Particle Track Reconstruction with Quantum Algorithms
Demirköz, Melahat Bilge; Carminati, Federico; Dobos, Daniel; Fracas, Fabio; Novotny, Kristiane; Potamianos, Karolos; Vallecorsa, Sofia; Vlimant, Jean-Roch (2020-11-16)
Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in as...
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Citation Formats
K. Novotny et al., “CTD2020: Exploring (Quantum) Track Reconstruction Algorithms for non-HEP applications,” presented at the Connecting the Dots Workshop (2020), 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69085.