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Giuseppe Carleo

EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne

EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne

EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne

EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne

EPFL SB IPHYS CQSL
PH H2 477 (Bâtiment PH)
Station 3
1015 Lausanne

Twitter

https://twitter.com/gppcarleo

Formation

PhD

| in Theory and Numerical Simulation of the Condensed Matter

2007 – 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

A dirigé les thèses EPFL de

Julien Sebastian Gacon, Dian Wu, Stefano Barison

Cours

Summer school: Simulating Many-Body Quantum Physics

PHYS-820

Computational quantum physics

PHYS-463

  1.<strong> Single-particle Problems:</strong> Numerical solutions of the Schroedinger equation, Numerov's integration, the split operator method   2. <strong>Quantum Spin Models</strong>: Choice and representations of basis sets for the many-body problem, the Trotter decompososition for real and imaginary-time evolution   3. <strong>Electronic Structure

Quantum physics IV

PHYS-426

Lecture series on scientific machine learning

PHYS-754

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

QUANT-400

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

Advanced computational physics

PHYS-339