Machine Learning Techniques for Many-Body Quantum Systems

PCTS Seminar Series: Deep Learning for Physics
Topic:Machine Learning Techniques for Many-Body Quantum Systems
Speaker:Giuseppe Carleo
Affiliation:CCQ, Flatiron Institute
Date:Tuesday, October 22
Time/Room:11:45am - 12:30pm/*Princeton University, 407 Jadwin Hall, PCTS Seminar Room*

In this introductory seminar I will cover the main machine learning techniques so-far adopted to study interacting quantum systems. I will first introduce the concept of neural-network quantum states [1], a representation of the many-body wave-function based on artificial neural networks. Theoretical aspects of these representations, including the problem of including symmetries, and their entanglement capacity will be discussed. Then, I will show how neural-network quantum states can be used in a variety of applications. Examples will be given for data-driven, experimental analysis in the context of quantum state tomography [2]. I will also show how these states can be used in variational applications to theoretically study the physical properties of interacting many-body matter, highlighting recent applications to frustrated magnetism [3] and fermionic systems [4].

    [1] Carleo, and Troyer - Science 355, 602 (2017); [2] Torlai, et al. - Nature Physics 14, 447 (2018); [3] Choo, et al. - arXiv:1903.06713 (2019); [4] Pfau, et al. - arXiv:1909.02487 (2019).