Fields of expertise
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 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).
in Theory and Numerical Simulation of the Condensed Matter
SISSA, International School for Advanced Studies, Trieste, Italy
2007 - 2011
|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