# Quantum graph neural networks

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**Quantum graph neural networks**

**Quantum graph neural networks**

Project **goal**

The goal of this project is to explore the feasibility of using quantum algorithms to help track the particles produced by collisions in the LHC more efficiently. This is particularly important as the rate of collisions is set to increase dramatically in the coming years.

**Collaborators**

Project **background**

The Large Hadron Collider (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 tenfold increase in the number of tracks produced per bunch crossing after the high-luminosity upgrade of LHC. Classical algorithms to perform the reconstruction of the trajectory of charged particles are making use of Kalman filter formalism and even though quite accurate, 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 leverage 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 data set designed for the recent Kaggle ‘TrackML’ challenge for this work.

Recent **progress**

We have established a consortium of parties interested in addressing this challenge. Members of the following organisations are now working together on this project: the Middle East Technical University (METU) in Ankara, Turkey; the University of Oxford, England; the California Institute of Technology (Caltech) in Pasadena, US; and gluoNNet, a humanitarian-focused big data analysis non-profit association based in Geneva, Switzerland. Quantum graph neural networks (QGNNs) can be implemented to represent quantum processes that have a graph structure. In the summer of 2019, we began work to develop a prototype QGNN algorithm for the tracking the particles produced by collision events.

Next **steps**

**Publications**

- C. Tüysüz, F. Carminati, B. Demirköz, D. Dobos, F. Fracas, K. Novotny, K. Potamianos, S. Vallecorsa, J.-R. Vlimant, A Quantum Graph Neural Network Approach to Particle Track Reconstruction. arXiv e-prints, p. arXiv:2007.06868 [cs.DC], 2020. cern.ch/go/6H88

**Presentations**

- F. Carminati, Particle Track Reconstruction with Quantum Algorithms (7 November). Presented at Conference on Computing in High Energy & Nuclear Physics, Adelaide, 2019. cern.ch/go/7Ddm

- D. Dobos, HEP Graph Machine Learning for Industrial & Humanitarian Applications (26 November). Presented at Conference on HEPTECH AIME19 AI & ML, Budapest, 2019. cern.ch/go/9Rvl

- C. Tuysuz, A Quantum Graph Neural Network Approach to Particle Track Reconstruction (22 January). Presented at the CERN openlab Technical Workshop, CERN, 2020. cern.ch/go/6TMH

- C. Tuysuz, A Quantum Graph Neural Network Approach to Particle Track Reconstruction (20 April). Presented at the 6th International Workshop “Connecting the dots”, Princeton University, 2020. cern.ch/go/9Tdt

- K. S. Novotny, Quantum Track Reconstruction Algorithms for non-HEP applications (29 July). Presented at the 40th International Conference on High Energy Physics, Prague, 2020. cern.ch/go/8HFG

- K. S. Novotny, Exploring (Quantum) Track Reconstruction Algorithms for non-HEP applications (21 April). Presented at the 6th International Workshop “Connecting the dots”, Princeton University, 2020. cern.ch/go/bMM