Giuseppe Carleo

Expertise Machine Learning for Quantum Physics; Numerical methods for strongly-correlated quantum systems; Quantum Computing; Characterization of Quantum Hardware; Dynamics of closed and open quantum systems; Frustrated magnets Expertise Machine Learning for Quantum Physics; Numerical methods for strongly-correlated quantum systems; Quantum Computing; Characterization of Quantum Hardware; Dynamics of closed and open quantum systems; Frustrated magnets Giuseppe Carleo is a computational quantum physicist, whose main focus is the development of advanced numerical algorithms to study challenging problems involving strongly interacting quantum systems.He is best known for the introduction of machine learning techniques to study both equilibrium and dynamical properties,based on a neural-network representations of quantum states, as well for the time-dependent variational Monte Carlo method.He earned a Ph.D. in Condensed Matter Theory from the International School for Advanced Studies (SISSA) in Italy in 2011.He held postdoctoral positions at the Institut d'Optique in France and ETH Zurich in Switzerland, where he alsoserved as a lecturer in computational quantum physics.In 2018, he joined the Flatiron Institute in New York City in 2018 at the Center for Computational Quantum Physics (CCQ), working as a Research Scientist and project leader, and also leading the development of the open-source project NetKet.Since September 2020 he is a professor at EPFL, in Switzerland, leading the Computational Quantum Science Laboratory (CQSL). Education PhD | in Theory and Numerical Simulation of the Condensed Matter 2007 – 2011 SISSA, International School for Advanced Studies, Trieste, Italy Master in Physics | 2005 – 2007 Sapienza University, Rome, Italy Bachelor in Physics | 2002 – 2005 Sapienza University, Rome, Italy Teaching & PhD PhD Students Gian Gentinetta, Linda Mauron, Samuele Piccinelli, Alessandro Sinibaldi, Shao Hen Chiew, David Linteau, Clemens Giuliani, Ali Goodarzi, Gianluca Grosso, Ruize Ma, Ekaterina Pankovets Past EPFL PhD Students Dian Wu, Julien Sebastian Gacon, Gabriel Maria Pescia, Barison Stefano, Imelda Romero Courses Advanced computational physics PHYS-339 The course covers dense/sparse linear algebra, variational methods in quantum mechanics, and Monte Carlo techniques. Students implement algorithms for complex physical problems. Combines theory with coding exercises. Prepares for research in computational physics and related fields. Computational quantum physics PHYS-463 The numerical simulation of quantum systems plays a central role in modern physics. This course gives an introduction to key simulation approaches, through lectures and practical programming exercises. Simulation methods based both on classical and quantum computers will be presented. Lecture series on scientific machine learning PHYS-754 This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.