CA24146 - Machine Learning and Quantum Computing for Future Colliders (MLQC4FC)

This COST Action aims to advance theoretical, experimental, and technological efforts for developing future particle colliders by leveraging cutting-edge computational technologies, including Machine Learning (ML) and Quantum Computing (QC). The action will bring together experts from High-Energy Physics (HEP), ML, and QC to form an interdisciplinary network that will explore and exploit synergies between emerging computing paradigms and HEP theory and experiments.

Future colliders pose complex challenges, from designing accelerators and experiments to managing and analyzing the enormous datasets they will produce. The large-scale data-taking and processing required in the next era of very-big-data physics will demand novel ML algorithms for efficient event selection, background reduction, pattern recognition, anomaly detection, and more. Additionally, QC offers possibilities for solving optimization problems related to collider design, simulating quantum systems, and improving theoretical predictions.

This COST Action will foster collaboration between theorists, experimentalists, and computational experts to address these challenges across all stages of collider research. It will focus on developing advanced ML techniques for real-time data analysis, enhancing theoretical predictions to match the precision expected in future experiments, and exploring QC applications in both theory and data processing.

Furthermore, the Action will play a key role in training and supporting Young Researchers and Innovators, fostering interdisciplinary and intergenerational collaboration to ensure long-term continuity and growth of the field. By building a strong European research network, this Action will accelerate scientific discoveries and push the frontiers of physics and technology.