Guisseppe Carleo - Simulating the Quantum World... (October 30, 2024)

Simulating the Quantum World with Data-Free, Physics-Driven Machine Learning
The behavior of electrons is chiefly responsible for the properties of materials and molecules. Predicting the behavior of many interacting electrons poses a significant scientific challenge and has led to the development of many methods of tackling problems in quantum many-body physics.
In this lecture, Giuseppe Carleo focused on simulation-driven machine learning techniques. He explored how artificial neural networks can represent quantum states and offered a powerful alternative to traditional variational methods. The talk introduced how these approaches systematically and controllably learn many-body wave functions without relying on pre-existing data. He examined applications in diverse domains, including condensed matter, chemistry and nuclear physics. Special attention was given to how neural network representations have advanced our ability to simulate prototypical many-body quantum systems, surpassing previous variational descriptions.