Speaker
Lorenzo Sestini
(Istituto Nazionale di Fisica Nucleare)
Description
In this talk, an overview of quantum machine learning (QML) activities developed within the LHCb experiment will be presented, with particular emphasis on their applications to classification and simulation tasks. Examples include the jet flavour identification as well as quantum generative models aimed at accelerating the detector simulations. It will be shown how quantum algorithms can potentially compete with state-of-the-art classical machine-learning methods in the LHCb analysis workflow. Finally, prospects and challenges for deploying QML tools on near-term quantum hardware and their integration into future data processing strategies at LHCb will be discussed.
| Sessions | Quantum Machine Learning: |
|---|---|
| Invited | No |
Author
Lorenzo Sestini
(Istituto Nazionale di Fisica Nucleare)