Ivano Tavernelli studied Biochemistry (M.Sc., 1991) and then Theoretical Physics (M.Sc., 1996) at the Eidgenossische Technische Hochschule Zürich (ETHZ), where he also obtained my Ph.D. in 1999. In 2000, he received a Marie Curie Fellowship to work in the group of Prof. Michiel Sprik at Cambridge University (UK). He then moved back to Switzerland to join the group of Prof. Ursula Roethlisberger, first at ETHZ (2002–2003) and later at the École Polytechnique Federale de Lausanne (EPFL, 2003-2014). In 2010, he earned the title of Maître d’Enseignement et de Recherche (MER) at EPFL. Finally in December 2014 he joined the IBM Research-Zurich laboratory in Rueschlikon, Swirzerland.

In the field of quantum-classical dynamics, he has developed and implemented in the CPMD software package a novel theoretical framework to combine electronic structure techniques based on density (DFT and TDDFT) with the calculation of nonadiabatic quantum and classical trajectories. His research interests in this field comprise adiabatic and nonadiabatic molecular dynamics (Ehrenfest dynamics, trajectory surface hopping, and Bohmian dynamics) for the study of photochemical and photo-physical processes in molecules, condensed phase, and biological systems. 

More recently, Tavernelli extended his research activities in the field of material design, focusing on the combination of ab-initio and machine learning techniques together with big-data analysis for the design of new materials with improved properties. This project is carried out within the swiss NCCR (National Centers of Competence in Research) project MARVEL.

Abstract:
The original idea that a quantum machine can potentially solve many-body quantum mechanical problems more efficiently than classical computers is due to R. Feynman who proposed the use of quantum computers to investigate the fundamental properties of nature at the quantum scale. In particular, the solution of problems in electronic structure, many-body physics, and high energy physics (just to mention a few) is a challenging computational task for classical computers as the number of needed resources increases exponentially with the number of degrees of freedom. More recently, the possibility of obtaining quantum speedup for the solution of classical optimization problems has opened up new research avenues in, e.g., classical statistical mechanics, machine learning and finance.
Thanks to the recent development of quantum technologies, we have now the possibility of addressing these classes of problems with the help of quantum computers. To achieve this goal, several quantum algorithms able to best exploit the potential quantum speedup of state-of-the-art, noisy, quantum hardware have been proposed.
After a short introduction on the state-of-the-art of digital quantum computing from a hardware and software prospective, I will present applications in many-body and high energy physics, focusing on those aspects that are relevant to achieve quantum advantage with near-term and fault tolerant quantum computers. In particular, I will focus on recent results in electronic structure calculations, lattice gauge theory, and quantum dynamics.

Light refreshments will be served after the talk on the LB terrace. All are welcome to attend.