Hadronic Jets: flavour and substructure

Hadronic jets, collimated sprays of particles produced in high-energy particle collisions, play a crucial role in the study of particle physics. In this PhD thesis, the focus is on two aspects of hadronic jets: flavour and substructure. The flavour of a jet refers to the identity of the initiating quark or gluon, and can provide information about the underlying physics processes that produced the jet. The substructure of a jet refers to its internal structure and is sensitive to the properties of the partons within the jet. The thesis presents a comprehensive study of the flavour and substructure of hadronic jets, including the development of new analysis techniques. Additionally, this thesis discusses the advancement of existing techniques for performing high-accuracy calculations for jet substructure observables, critical for comparing theoretical predictions with experimental measurements. Recently, there has been a growing interest in machine learning and quantum computing techniques applied to jet physics or other close-related subjects. Machine learning algorithms are increasingly being used to identify and classify jets, while quantum computers have the potential to revolutionize high-precision calculations in jet physics. This thesis discusses a novel method to calculate the gradient of a function on quantum computers, further advancing the use of quantum computing in jet physics. The results of this study are ready for further phenomenological applications, contributing to a better understanding of the properties of hadronic jets and the physics of high-energy particle collisions.