Quantum Machine Learning Integration in the High Energy Physics Pipeline

This seminar is part of the BasQ-IBM Quantum Research Seminar series

CERN has started its second phase of the Quantum Technology Initiative with a 5-year term plan aligned with the CERN research and collaboration objectives. The integration of Quantum Machine Learning (QML) into the High Energy Physics (HEP) pipeline represents a transformative approach to addressing computational challenges in the analysis of vast and complex datasets. This talk will walk through main research directions and results from theoretical foundations of quantum machine learning algorithms to application in several areas of HEP, showing where QML has been applied to HEP challenges, such as anomaly detection, data generation, and will outline future directions for incorporating quantum technologies into the broader HEP research framework and beyond.

Zoom: https://dipc-org.zoom.us/j/99435152344