Speaker
Donatella Lucchesi
Description
Quantum Machine Learning (QML) algorithms are proposed as an exciting prospective application of quantum technologies. Problems that are classically hard to compute have potential to be solved faster by such new algorithms. The LHCb collaboration has already made a first step by studying the identification of b and b-bar jets showing new possibilities opened by these algorithms.
This new project proposes to demonstrate how QML can improve the determination of the Higgs boson decay rates to b and c jets by exploiting the different b and c jet sub-structure.
| CERN group or section submitting a project proposal | quantum computing and algorithms |
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Primary authors
Alessio Gianelle
(Universita e INFN, Padova (IT))
Davide Nicotra
(Universita e INFN, Padova (IT))
Donatella Lucchesi
Carlos Vazquez Sierra
(CERN)
Davide Zuliani
(Universita e INFN, Padova (IT))
Eduardo Rodrigues
(University of Liverpool (GB))
Jacco Andreas De Vries
(Universiteit Maastricht (NL))
Lorenzo Sestini
(Universita e INFN, Padova (IT))
