Giuseppe Carleo

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 to
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 a 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

Chiew Shao Hen, Gentinetta Gian Florin, Giuliani Clemens, Linteau David, Mauron Linda, Pescia Gabriel Maria, Piccinelli Samuele, Romero Imelda, Sinibaldi Alessandro,

Past EPFL PhD Students

Barison Stefano , Gacon Julien Sebastian , Wu Dian ,

Courses

Advanced computational physics

The course covers dense/sparse linear algebra, variational methods in quantum mechanics, and Monte Carlo techniques. Students implement algorithms for complex physical problems. Combines theory with coding exercises. Prepares for research in computational physics and related fields.

Quantum physics IV

Introduction to the path integral formulation of quantum mechanics. Derivation of the perturbation expansion of Green's functions in terms of Feynman diagrams. Several applications will be presented, including non-perturbative effects, such as tunneling and instantons.

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

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.

Summer school: Simulating Many-Body Quantum Physics

The course covers classicalquantum methods for simulating quantum many-body systems, including Quantum Monte Carlo (Variational Monte Carlo) neural-and tensor network wavefunctions, quantum algorithms, and error mitigation. Participants enhance practical skills and interdisciplinary collaboration.

Introduction to quantum science and technology

Introduction (2 weeks):

Overview of the frontiers of quantum science, technology and applications.
Introduction to qubits, quantum states, measurements, evolution. Axiomatic formulation.
Illustration with two level systems, Bloch sphere, Spin, its manipulation in magnetic fields. Heisenberg and spin Hamiltonians, eleme