Prof. Giuseppe Carleo

CIS – “Get to know your neighbors” Seminar Series

“Machine learning in quantum science and quantum computing”

Giuseppe Carleo,
Tenure Track Assistant Professor, Computational Quantum Science Laboratory

Monday 11th January 2021 – 3:15 – 4h15pm (CET)

Machine-learning-based approaches, routinely adopted in cutting-edge industrial applications, are being increasingly embraced to study fundamental problems in science. Quantum physics is very much at the forefront of these exciting developments, given its intrinsic “big-data” nature.

In this seminar I will present the core concepts and the main challenges arising in the field. First, I will recall the concept of quantum wave function, and why its theoretical description poses a fundamental challenge in the understanding of many natural phenomena.

I will then discuss the role of machine learning methods, and introduce the notion of neural-network quantum states, a novel tool to address the complexity of the wave function. Applications and open challenges in diverse domains will be presented, ranging from Condensed Matter physics to Quantum Computing. Focusing on the latter case, I will show that there are relevant cases in which machine learning techniques can be used to classically simulate useful quantum algorithms, and I will discuss their role in assessing the so-called “quantum supremacy” limits of quantum computers.

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 having shaped the field of machine learning methods for many-body quantum phenomena, with the introduction of neural-network representations of quantum states in 2016.

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.
From 2018 to 2020, he was a staff Research Scientist and project leader at the Flatiron Institute in New York City, at the Center for Computational Quantum Physics (CCQ), where he also led the development of the open-source project NetKet.

He has recently joined EPFL as an assistant professor, leading a research group focused on computational quantum science.