Quantum jet clustering with LHC simulated data

de Lejarza, Jorge J. Martínez; Cieri, Leandro; Rodrigo, Germán

doi:10.22323/1.414.0241

Quantum jet clustering with LHC simulated data

Sep 19, 2022

6 pages

Published in:

PoS ICHEP2022 (2022) 241

Contribution to:

- ICHEP 2022
, 241
,
- ICHEP 2022

Published: Sep 22, 2022

e-Print:

2209.08914 [hep-ph]

DOI:

10.22323/1.414.0241

Report number:

IFIC/22-26

View in:

ADS Abstract Service

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Abstract: (SISSA)

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.

Note:

6 pages, 1 figure, 1 table, Contribution to 41st International Conference on High Energy physics - ICHEP 2022, 6-13 July 2022, Bologna, Italy

event shape analysis
computer: quantum
tracks
efficiency
CERN LHC Coll
kT algorithm

References(14)

Figures(1)

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