Quantum Machine Learning for $b$-jet charge identification

Understand

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.