Machine-learning-based approaches, routinely adopted in cutting-edge industrial applications, are being increasingly adopted to study fundamental problems in science. Many-body physics is very much at the forefront of these exciting developments, given its intrinsic "big-data" nature. In this seminar I will present selected applications to the quantum realm. First, I will discuss how a systematic, and controlled machine learning of the many-body wave-function can be realized. This goal is achieved by a variational representation of quantum states based on artificial neural networks. I will then discuss applications in diverse domains, including prototypical open problems in Condensed Matter physics -- fermions and frustrated spins -- as well as applications to characterize and improve quantum hardware and software.
Argonne Physics Division Colloquium Schedule