Quantum Computing and Quantum Machine Learning for High-Energy Physics

por Prof. Michael Spannowsky (Durham University)

jueves 27 nov. 2025 12:30 → 13:30 Europe/Madrid

Salón de Actos (PCUV)

Quantum computing is reshaping how we think about computation in fundamental physics. In high-energy physics, it offers new routes to simulate strongly interacting quantum systems, accelerate data analysis, and design learning architectures inspired by quantum mechanics itself. This talk will outline how key field-theoretic problems, from lattice gauge dynamics and non-perturbative evolution to parton-shower modeling, can be reformulated for quantum devices using gate-based and annealing paradigms. A second focus will be quantum machine learning, where variational quantum circuits, quantum-informed neural networks, and hybrid quantum-classical optimizers are explored as tools for collider event classification and anomaly detection. I will present recent implementations on quantum hardware, discuss their performance relative to classical methods, and highlight how quantum geometry and Fisher information concepts provide a unifying framework for simulation and learning. Together, these developments outline steps toward integrating quantum computing into the experimental and theoretical workflow of particle physics.

Link to the colloquium