Quantum Machine Learning for data analysis at LHCb

Machine learning (ML) algorithms have now become crucial in the field of High Energy Physics (HEP). An area where the application of such algorithms has proven particularly beneficial is the classification of hadronic jets produced at the Large Hadron Collider (LHC). Considering the complexity of the tasks in this field and the impending Run 3 data at higher luminosity, it is evident that a step-up in computational power is imperative. One potential candidate comes from the intersection between Quantum Computing (QC) and ML. Quantum Machine Learning (QML) algorithms leverage the intrinsic properties of QC, such as superposition and entanglement, to achieve better performance compared to their classical counterparts. This work provides an overview of these new learning models, with a focus in HEP. Specifically, we present studies of QML applications for the classification of jets produced (b vs. b̄ and b vs. c) at the LHCb experiment. Notably, we discuss recent developments in measuring entanglement entropy between qubits to gain new insights from the jet events data.