Speaker
Description
The search for supersymmetric particles is one of the major goals of the
Large Hadron Collider (LHC). Supersymmetric top searches play a very
important role in this respect, but the unprecedented collision rate to be
attained at the next high luminosity phase of the LHC poses new challenges
for the separation between any new signal and the standard model
background. Quantum computing techniques may yield an efficient solution
for this problem. In this talk, I will present a novel application of a
binary classification based on quantum annealing machine learning to
discriminate a stop signal from the background, and its implementation in
a quantum annealer machine. This approach together with the pre-processing
of the data with principal component analysis may yield better results
than conventional multivariate approaches.
