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 will focus on simulation-driven machine learning techniques. He’ll explore how artificial neural networks can represent quantum states, offering a powerful alternative to traditional variational methods. The talk will introduce how these approaches systematically and controllably learn many-body wave functions without relying on pre-existing data. He’ll examine applications in diverse domains, including condensed matter, chemistry and nuclear physics. Special attention will be given to how neural network representations have advanced our ability to simulate prototypical many-body quantum systems, surpassing previous variational descriptions.