EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||9|
|Section||10 - Crossover sessions from online, offline and exascale|
|Published online||16 November 2020|
Quantum annealing algorithms for track pattern recognition
International Center for Elementary Particle Physics (ICEPP), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
2 Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
3 Department of Physics, University of California, Berkeley, CA 94720, USA
4 Haute ecole d’Ingénierie et d’Architecture de Fribourg, Boulevard de Pérolles 80, 1705 Fribourg, Switzerland
* e-mail: email@example.com
Published online: 16 November 2020
The High-Luminosity Large Hadron Collider (HL-LHC) starts from 2027 to extend the physics discovery potential at the energy frontier. The HL-LHC produces experimental data with a much higher luminosity, requiring a large amount of computing resources mainly due to the complexity of a track pattern recognition algorithm. Quantum annealing might be a solution for an efficient track pattern recognition in the HL-LHC environment. We demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer. The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. The tracking efficiency and purity for the Fujitsu Digital Annealer are nearly the same as the classical simulated annealing.
© The Authors, published by EDP Sciences, 2020
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