| Issue |
EPJ Web Conf.
Volume 274, 2022
XVth Quark Confinement and the Hadron Spectrum Conference (ConfXV)
|
|
|---|---|---|
| Article Number | 09002 | |
| Number of page(s) | 8 | |
| Section | 9 - Parallel Track H | |
| DOI | https://doi.org/10.1051/epjconf/202227409002 | |
| Published online | 22 December 2022 | |
https://doi.org/10.1051/epjconf/202227409002
Vertex reconstruction in particle detectors using quantum computing algorithms
1 Universidad de Cantabria
2 Instituto de Física de Cantabria, UC-CSIC
* e-mail: francisco.matorras@alumnos.unican.es
** e-mail: parbol@ifca.unican.es
Published online: 22 December 2022
This work aims at testing a new quantum computing algorithm to reconstruct vertices in the context of a tracker-like particle detector. Input tracks have been generated using a simplified tracker simulator assuming they originate from two different vertices. The Point of Closest Approach of the tracks to the beam line has been considered as the nodes of a graph connected to the other nodes through a weight equivalent to the euclidean distance between the two. A Variational Quantum Eigensolver algorithm has been used in order to divide the graph in two groups that maximizes the total distance between the two groups. The algorithm has been implemented using Qiskit, the IBM framework, obtaining a track-vertex association accuracy of about 90% for distances between vertices of a few milimeters. This work represents a simple proof-of-concept that a quantum computing algorithm can be used to solve the problem of the vertex reconstruction.
© The Authors, published by EDP Sciences, 2022
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