SCIENTIFIC PROGRAMME
The MLQC4FC Training School will provide an provide a comprehensive introduction to the application of Machine Learning and Quantum Computing for future collider experiments, combining lectures by leading experts with hands-on tutorial sessions.
The scientific programme will cover five main thematic areas:
- Machine Learning for Particle Physics, including the foundations of machine learning, anomaly detection, surrogate modelling, differentiable programming, spiking neural networks, and fast inference for resource-constrained environments.
- Quantum Computing for High Energy Physics, introducing the fundamentals of quantum computing and quantum machine learning, together with applications of quantum technologies, quantum sensing, and quantum optimization for particle physics.
- Computational Infrastructure and Resource Optimization, focusing on efficient use of modern computing infrastructures, including GPUs, high-performance computing systems, code profiling and optimization, FPGA technologies, and sustainable computing.
- Collider Physics, providing an introduction to Monte Carlo simulations, modern collider phenomenology, precision measurements, and the physics programme of future collider facilities.
- Theory–Experiment Interface, covering modern reinterpretation (recasting) techniques, analysis preservation, and software tools enabling the comparison of theoretical models with experimental data.
In addition to the scientific lectures, participants will take part in practical hands-on tutorials allowing participants to gain practical experience with modern software tools and computational techniques used in particle physics research.