The LHC at CERN is producing collisions at unprecedented collider energy. The hundreds of particles created during the collisions are recorded by large detectors composed of several sub-detectors. At the centre of these detectors there is usually a tracker detector, precisely recording the signal of the passage of charged particles through thin layers of active material. The trajectories of particles are bent by a magnetic field to allow the measurement of the particle momentum. There is an expected ten-fold increase in the number of tracks produced per bunch crossing after the high-luminosity upgrade of LHC.

Classical algorithms perform the reconstruction of the trajectory of charged particles; these make use of Kalman filter formalism. Although they are quite accurate, they scale worse than quadratically with the number of tracks. Several ways are explored to mitigate the increase in the computing needs, such as new detector layout, deep-learning and code parallelisation. Quantum computing has been shown to provide speed-ups for certain problems and different R&D initiatives are exploring how quantum tracking algorithms could exploit such capabilities. We are developing a quantum-based track-finding algorithm aimed at reducing the combinatorial background during the initial seeding stage for the Kalman filters. We are using the publicly available dataset designed for the Kaggle ‘TrackML’ challenge for this work.