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
Site web: 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
Site web: 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-GE
Site web: https://go.epfl.ch/phd-edpy
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
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).
Formation
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
Publications représentatives
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
Enseignement et PhD
Current Phd
Gian Gentinetta, Linda Mauron, Samuele Piccinelli, Alessandro Sinibaldi, Shao Hen Chiew, David Linteau, Imelda Romero, Clemens Giuliani
Past Phd As Director
Dian Wu, Julien Sebastian Gacon, Gabriel Maria Pescia, Barison Stefano
Courses
Advanced computational physics
PHYS-339
Le cours couvre l'algèbre linéaire dense/creuse, les méthodes variationnelles en mécanique quantique et les techniques Monte Carlo. Les étudiants implémentent des algorithmes pour des problèmes physiques complexes. Allie théorie et exercices de programmation.
Computational quantum physics
PHYS-463
La simulation numérique des systèmes quantiques est essentielle en physique moderne. Ce cours offre une introduction aux approches de simulation principales, combinant cours théoriques et exercices pratiques, utilisant des ordinateurs classiques et quantiques.
Lecture series on scientific machine learning
PHYS-754
Ce cours présente des travaux sur la façon dont les questions scientifiques peuvent être abordées à l'aide de l'apprentissage automatique. L'apprentissage automatique permet d'extraire des connaissances à partir de données de manière automatisée. Nous apprendrons à partir d'exemples concrets.
Quantum physics IV
PHYS-426
Introduction à la formulation de la mécanique quantique comme une intégrale de chemin. Dérivation de l'expansion de perturbation des fonctions de Green en termes de diagrammes de Feynman. Plusieurs applications seront présentées, y compris certains effets non perturbateurs.