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Particle Track Reconstruction with Quantum Algorithms

Machine Learning, Quantum Computing, R&D Project

PARTICLE TRACK RECONSTRUCTION WITH QUANTUM ALGORITHMS

Together with CERN openlab, Middle East Technical University (METU), Caltech, and Deutsches Elektronen-Synchrotron (DESY), we are aiming for novel solutions to the particle track reconstruction problem in High Energy Physics experiments using Quantum Computing. This project won the CERN openlab Poster Prize at the CERN openlab Annual Workshop 2020

More detailed information about the project can be found here.

THE CHALLENGE
The necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments is to increase, due to an unprecedented increase in complexity and scale of data. Therefore, faster algorithms for particle track reconstruction are on demand.

OUR SOLUTION
We use Quantum Graph Neural Networks for particle track reconstruction. We aim to leverage quantum computing’s capability to evaluate a vast number of states simultaneously and thus effectively search in larger parameter space.

THE ADDED BENEFIT
Using Quantum Graph Neural Networks, we provide the first attempt to particle track reconstruction problem using gate-type Quantum Computers.

Latest news on our R&D collaboration with CERN openlab