Giuseppe Carleo
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne
+41 21 693 93 96
Office: PH H2 477
EPFL › SB › IPHYS › CQSL
Website: https://www.epfl.ch/labs/cqsl/
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne
+41 21 693 93 96
Office: PH H2 477
EPFL › SB › SB-SPH › SPH-ENS
Website: https://sph.epfl.ch/
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne
+41 21 693 93 96
Office: PH H2 477
EPFL › STI › STI-SSIQ › STI-SSIQ-GE
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne
+41 21 693 93 96
Office: PH H2 477
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDPY-ENS
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne
+41 21 693 93 96
Office: PH H2 477
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDPY-GE
Website: https://go.epfl.ch/phd-edpy
Expertise
Expertise
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 a professor at EPFL, in Switzerland, leading the Computational Quantum Science Laboratory (CQSL).
Education
PhD
| in Theory and Numerical Simulation of the Condensed Matter2007 – 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
Selected publications
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo, and Matthias Troyer
Published in Science, 355:602, 2017 in
Neural-network quantum-state tomography
Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla, Matthias Troyer, Roger Melko, and Giuseppe Carleo
Published in Nature Physics, 14:447, 2018 in
Machine learning and the physical sciences
Giuseppe Carleo et al.
Published in Reviews of Modern Physics, 91:045002, 2020 in
Localization and Glassy Dynamics Of Many-Body Quantum Systems
Giuseppe Carleo, Federico Becca, Marco Schiró, and Michele Fabrizio
Published in Scientific Reports 2, 243 (2012) in
Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
Michael J. Hartmann, and Giuseppe Carleo
Published in Phys. Rev. Lett. 122, 250502, 2019 in
Quantum Natural Gradient
James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo
Published in Quantum 4, 269 (2020) in
PhD Students
David Linteau, Samuele Piccinelli, Linda Mauron, Gian Florin Gentinetta, Gabriel Maria Pescia, Clemens Giuliani, Imelda Romero, Alessandro Sinibaldi, Shao Hen Chiew
Past EPFL PhD Students
Courses
Summer school: Simulating Many-Body Quantum Physics
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.
Quantum physics IV
Lecture series on scientific machine learning
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.
Introduction to quantum science and technology
This course provides all students with a broad view of the diverse aspects of the field: quantum physics, communication, computation, simulation, quantum hardware technologies, quantum sensing and metrology. The course will be an overview of frontiers of the domain and taught by multiple instructors