Speaker
Description
Quantum Machine Learning is emerging as a promising paradigm for advancing data analysis, simulation, and optimization in high energy physics and accelerator-based experiment. This presentation provides an overview of ongoing QML efforts within INFN High Energy Particle Physics with Accelerators Commission (CSN1) and across Italian institutions. I will highlight existing projects developing quantum algorithms for classification, anomaly detection, event reconstruction and simulation, and quantum-enhanced optimization, with connections to LHC and beyond-LHC physics programs. The talk aims to give a unified picture of the current landscape in CSN1, mapping current strengths and collaborations, and outline opportunities for synergy within INFN and with international initiatives.
| Sessions | Quantum Machine Learning: |
|---|---|
| Invited | Yes |