Giuseppe Carleo
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
CH-1015 Lausanne
Web site: Web site: https://www.epfl.ch/labs/cqsl/
EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
CH-1015 Lausanne
Web site: Web site: https://sph.epfl.ch/
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 Students
Barison Stefano, Gacon Julien Sebastian, Gentinetta Gian Florin, Giuliani Clemens, Pescia Gabriel Maria, Romero Imelda, Wu Dian,Courses
Quantum physics I
The objective of this course is to familiarize the student with the concepts, methods and consequences of quantum physics.
Computational quantum physics
1. Single-particle Problems: Numerical solutions of the Schroedinger equation, Numerov's integration, the split operator method
2. Quantum Spin Models: Choice and representations of basis sets for the many-body problem, the Trotter decompososition for real and imaginary-time evolution
3. Electronic Structure
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