Carleo, Giuseppe

Date:   Thursday, April 15, 2021
Time:   10:00
Place:   scheduled Zoom meeting
Host:    Klaus Ensslin

Machine-Learning for Many-Body Quantum Physics

Giuseppe Carleo
EPF Lausanne

Machine-learning-based approaches, routinely adopted in cutting-edge industrial applications, are being increasingly embraced 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, ranging from open problems in Condensed Matter physics to applications in Quantum Computing. Focusing on the latter case, I will show that there are relevant cases in which machine learning techniques can be already used to classically simulate useful quantum algorithms, using purely classical resources.