PoS - Proceedings of Science
Volume 414 - 41st International Conference on High Energy physics (ICHEP2022) - Computing and Data Handling
Quantum jet clustering with LHC simulated data
J. Martínez de Lejarza*, L. Cieri and G. Rodrigo
Full text: pdf
Pre-published on: September 22, 2022
Published on: June 15, 2023
Abstract
We study the case where quantum computing algorithms could improve jet clustering by considering two new quantum algorithms that might speed up classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, while the second one consists of a quantum circuit to track the rough maximum into a list of unsorted data. When one or both algorithms are implemented in classical versions of well-known clustering algorithms (K-means, Affinity Propagation and $k_T$-jet) we obtain efficiencies comparable to those of their classical counterparts. Furthermore, in the first two algorithms, an exponential speed up in dimensionality and data length can be achieved when applying the distance or the maximum search algorithm. In the $k_T$ algorithm, a quantum version of the same order as FastJet is achieved.
DOI: https://doi.org/10.22323/1.414.0241
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.