  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.
