Many-body quantum physics with machine learning
Aula Renzo Leonardi
ECT*
Attention
If you received emails from travellerpoint(dot)org, please be careful. The email asks about arrival and departure dates to Trento. Please ignore the emails and do not reply or click on any link given by them.
Machine learning techniques have become standard scientific tools across several fields. Quantum many-body theory is no exception, with a recent explosion of applications in domains that range from spin systems, to quantum chemistry and nuclear physics. This workshop is devoted to discussing machine-learning tools that aim at directly solving the Schrödinger equation in a many-particle context. These tools typically exploit the outstanding variational properties of neural networks, including first- and second-quantized versions. We aim at bringing together quantum many-body practitioners, within and outside the nuclear domain, and physics-focused machine learning experts, to review past achievements and recent advances in solving quantum many-body problems with machine learning tools. By bringing together different communities, we want to take a global look at the state-of-the-art in the field; to identify new avenues and recent meaningful advances; and to stimulate cross-fertilisation among fields to consolidate the use of machine-learning tools in the many-body domain.
.png){kind=logo}
Alessandro Lovato
Alessandro Roggero
Alessandro Sinibaldi
ALVARO SAIZ CASTILLO
Amir Azzam
Arnau Rios Huguet
Bryce Fore
Carlo Barbieri
Clemens Giuliani
Daniel Lima
David Linteau
David Pfau
David Regnier
Dennis Westerbeck
Di Luo
Elad Parnes
Estelle Inack
Fabian Ballar Trigueros
Filippo Vicentini
Florian Andreas Franz Schöppl
Francesco Pederiva
Georg Maier
Giuseppe Carleo
James Keeble
Javier Rozalén Sarmiento
Jonas Rigo
Maciej Marciniak
Markus Holzmann
Mathias Steinhuber
Mehdi Drissi
Pascal Vecsei
Pedro Pérez-Fernández
Pengsheng Wen
Rajat Panda
Roberto Verdel Aranda
Sebastiano Pilati
Sidhartha Dash
