Quantum Machine Learning for $b$-jet charge identification

• URL: http://arxiv.org/abs/2202.13943v2
• Date: Sun, 2 Oct 2022 21:52:38 GMT
• Title: Quantum Machine Learning for $b$-jet charge identification
• Authors: Alessio Gianelle (1), Patrick Koppenburg (2), Donatella Lucchesi (1 and 3), Davide Nicotra (3 and 4), Eduardo Rodrigues (5), Lorenzo Sestini (1), Jacco de Vries (4), Davide Zuliani (1 and 3 and 6) ((1) INFN Sezione di Padova, Padova, Italy, (2) Nikhef National Institute for Subatomic Physics, Amsterdam, Netherlands, (3) Universit\`a degli Studi di Padova, Padova, Italy, (4) Universiteit Maastricht, Maastricht, Netherlands, (5) University of Liverpool, Liverpool, United Kingdom, (6) European Organization for Nuclear Research (CERN), Geneva, Switzerland)
• Abstract summary: We present a new approach to identify if a jet contains a hadron formed by a $b$ or $barb quark at the moment of production, based on a Variational Quantum applied to simulated data of the LHCb experiment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context Quantum Machine Learning is a new and almost unexplored methodology, where the intrinsic properties of quantum computation could be used to exploit particles correlations for improving the jet classification performance. In this paper, we present a brand new approach to identify if a jet contains a hadron formed by a $b$ or $\bar{b}$ quark at the moment of production, based on a Variational Quantum Classifier applied to simulated data of the LHCb experiment. Quantum models are trained and evaluated using LHCb simulation. The jet identification performance is compared with a Deep Neural Network model to assess which method gives the better performance.

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