Giuseppe Carleo
Fields of 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
Biography
Giuseppe Carleo is a computational quantum physicist, whose main focus is the development of advanced numerical algorithms tostudy 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 also
served 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 an assistant professor at EPFL, in Switzerland, leading the Computational Quantum Science Laboratory (CQSL).
Education
PhD
in Theory and Numerical Simulation of the Condensed Matter
SISSA, International School for Advanced Studies, Trieste, Italy
2007 - 2011
Publications
Selected publications
| Giuseppe Carleo, and Matthias Troyer Science, 355:602, 2017 |
Solving the quantum many-body problem with artificial neural networks |
| Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla, Matthias Troyer, Roger Melko, and Giuseppe Carleo Nature Physics, 14:447, 2018 |
Neural-network quantum-state tomography |
| Giuseppe Carleo et al. Reviews of Modern Physics, 91:045002, 2020 |
Machine learning and the physical sciences |
| Giuseppe Carleo, Federico Becca, Marco Schiró, and Michele Fabrizio Scientific Reports 2, 243 (2012) |
Localization and Glassy Dynamics Of Many-Body Quantum Systems |
| Michael J. Hartmann, and Giuseppe Carleo Phys. Rev. Lett. 122, 250502, 2019 |
Neural-Network Approach to Dissipative Quantum Many-Body Dynamics |
| James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo Quantum 4, 269 (2020) |
Quantum Natural Gradient |
Teaching & PhD
Teaching
Physics
PhD Programs
Doctoral Program in Physics
Doctoral program in computer and communication sciences
PhD Students
Barison Stefano, Gacon Julien Sebastian, Giuliani Clemens, Pescia Gabriel Maria, Romero Imelda, Wu Dian,Courses
Computational quantum physics
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
Machine learning is a data analysis and computational tool that in the last two decades brought groundbreaking progress into many modern technologies. What is more, machine learning is becoming an indispensable tool enabling progress in many scientific disciplines where knowledge is deduced from data.
This course will present some recent works in this direction. In the first part of the