# A Quantum Graph Neural Network Approach to Particle Track Reconstruction

@article{Tuysuz2020AQG, title={A Quantum Graph Neural Network Approach to Particle Track Reconstruction}, author={Cenk Tuysuz and Federico Carminati and Bilge Demirkoz and Daniel Dobos and Fabio Fracas and Kristiane Novotny and Karolos Potamianos and Sofia Vallecorsa and J. R. Vlimant}, journal={arXiv: Quantum Physics}, year={2020} }

Unprecedented increase of complexity and scale of data is expected in computation necessary for the 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… Expand

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#### 5 Citations

Hybrid Quantum-Classical Graph Convolutional Network

- Computer Science, Physics
- ArXiv
- 2021

This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data that demonstrates an advantage over classical multilayer perceptron and convolutionAL neural networks in the aspect of number of parameters. Expand

Investigating Quantum Speedup for Track Reconstruction: Classical and Quantum Computational Complexity Analysis

- 2021

Physics of Information and Quantum Technologies Group, Instituto de Telecomunicações, Portugal Instituto Superior Técnico, Universidade de Lisboa, Portugal Department of Mathematics, Clarkson… Expand

Quantum speedup for track reconstruction in particle accelerators

- Physics
- 2021

D. Magano, 2 A. Kumar, 3 M. Kālis, A. Locāns, A. Glos, S. Pratapsi, 2 G. Quinta, M. Dimitrijevs, A. Rivošs, P. Bargassa, 6 J. Seixas, 7 A. Ambainis, and Y. Omar 2 Physics of Information and Quantum… Expand

Performance of Particle Tracking Using a Quantum Graph Neural Network

- Physics, Computer Science
- 2020

This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid graph Neural Network in order to benefit the exponentially growing Hilbert Space. Expand

Quantum annealing algorithms for track pattern recognition

- Computer Science
- 2020

This work demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer, finding a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. Expand

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