Abstract:

Abstract: Machine learning (ML) techniques have become a ubiquitous tool with applications in many different areas. In the past few years, quantum physicists have started to use a variety of ML methods for example for experimental control and design, numerical simulation, and data analysis. In this talk, I will focus on the application of ML to quantum data analysis, in particular in the context of quantum many-body systems. Recent advances in quantum simulation experiments have paved the way for a new perspective on strongly correlated quantum many-body systems. Digital as well as analog quantum simulation platforms are capable of preparing desired quantum states, and various experiments are starting to explore equilibrium states as well as non-equilibrium many-body dynamics in previously inaccessible regimes. Quantum simulators can provide single-site resolved quantum projective measurements of the state, and thus a huge amount of information on the quantum many-body state. I will introduce different ML based approaches to make use of this information to classify quantum data, compare experimental results to theoretical approaches, and gain physical insights through interpretability. 

Bio:

Annabelle Bohrdt is a theoretical physicist aiming for a microscopic understanding of strongly correlated quantum systems by developing new analysis tools. In her research, she combines numerical methods, intuitive physical pictures, close collaboration with quantum simulation experiments, and machine learning techniques. She obtained her doctoral degree from Technical University Munich (Germany). During her PhD, Annabelle spent two years as an exchange student in the group of Eugene Demler at Harvard. From 2021 to 2023, she was an independent ITAMP postdoctoral fellow at Harvard University. Since 2023, she is a professor for theoretical physics at the University of Regensburg, Germany.