Embedding of particle tracking data using hybrid quantum-classical neural networks
1 ETH Zürich, Otto-Stern-Weg 1, Zürich, Switzerland
2 Middle East Technical University, Dumlupınar Bulvarı, Çankaya, Ankara, Turkey
3 gluoNNet, Avenue de Sécheron 15, Geneva, Switzerland
4 CERN, 1 Esplanade des Particules, Geneva, Switzerland
5 University of Oxford, Parks Rd, Oxford, United Kingdom
6 Lancaster University, Bailrigg, Lancaster, United Kingdom
7 Caltech, 1200 East California Boulevard Pasadena, California, United States
Published online: 23 August 2021
The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.