# abstract

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].