Measurement of the c c-bar charge asymmetry with the LHCb detector and Quantum Machine Learning methods

Thanks to the excellent tracking and calorimeter systems, LHCb can precisely reconstruct and identify hadronic jets in the forward region of proton-proton collisions. Moreover, LHCb has demonstrated that it can precisely separate b-jets (jets generated from b quarks) from b-bar-jets (generated from anti-b quarks) by the means of Quantum Machine Learning (QML) algorithms. In this thesis an algorithm, based on QML methods, for separating c-jets and c-bar-jets will be developed. This task is fundamental for measuring the so-called c c-bar charge asymmetry, an observable sensitive to new physics contribution in the Electroweak sector, that has never been measured before. Its determination could be also used to determine the intrinsic charm component of the proton. In this work the performance of the “classical” and quantum algorithms for c-jets identification will be compared, and the expected LHCb sensitivity on the measurement of the cc-bar charge asymmetry with the new algorithms will be determined.