Quantum100 ⊗ AI Workshop

High-energy colliders, such as the Large Hadron Collider (LHC) at CERN, are genuine quantum machines by nature, and thus, following Richard Feynman’s original motivation for quantum computing, the scattering processes occurring there should be more effectively simulated by a quantum system. While the dream of a fully-fledged quantum event generator for simulating scattering processes at colliders is still far in future, there is a huge interest in the particle physics community in leveraging the latest advances in Quantum Computing. The potential applications include quantum machine learning techniques for collider data analysis, enabling faster and more precise evaluations of the intricate multiloop Feynman diagrams, simplifying the complexity of jet clustering, simulating parton showers, and many others. In this talk, I will focus on two specific applications, the identification of the causal structure of multiloop Feynman diagrams, a fundamental ingredient in the Loop-Tree Duality which is closely linked to graph theory, and the integration and sampling of multidimensional functions. The latter represents an initial step toward realizing a partonic quantum event generator with next-to-leading order (NLO) accuracy and beyond.