Abstract:

CERN has almost complete the third years of its Quantum Technology Initiative,  investigating the usage of quantum technologies in High Energy Physics (HEP). A three-year roadmap and research program, in collaboration with the HEP and quantum-technology research communities with the goal of establish initial pilot projects. Focusing mainly on Quantum Computing and Quantum Machine Learning projects, we contributed to the development of prototypes of quantum algorithms, which are being evaluated in the context of LHC experiments for different types of workloads.  In this talk I will provide an overview of recent results obtained, including open source contributions for applications of anomaly detection, noisy gates, accelerator beams optimisation, and nuclear physics.

Bio:

Michele Grossi is a senior fellow in quantum computing and algorithm at CERN. He received his industrial PhD in High Energy Physics from the University of Pavia working on quantum machine learning models for boson polarisation discrimination.  Michele has worked for several years as Quantum Technical Ambassador and Hybrid Cloud solution Architect at IBM.  In his current role at CERN he leads a young rgroup of researchers focusing on application of quantum algorithm. His focus is the development of Quantum Machine Learning pipelines for High Energy Physics problems and beyond, in collaboration with industries and universities.  In 2019 Forbes selected Michele as one of the top 30 under 30 young Italian leaders in enterprise technology.