Getting the most out of your measurements: neural networks and active learning

Event Description



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 previously inaccessible regimes in terms of system sizes and time scales. State-of-the art quantum simulators provide single-site resolved quantum projective measurements of the state. Depending on the platform, measurements in different local bases are possible. The question emerges which observables are best suited to study such quantum many-body systems. In this talk, I will cover two different approaches to make the most use of these possibilities. In the first part, I will discuss a new perspective on the doped Fermi-Hubbard model through the lens of real space images. I will introduce a simple semi-analytical theory to describe a single dopant, and use machine learning techniques to compare different theoretical approaches to experimental data from a quantum gas microscope. In the second part of this talk, I will present a scheme to perform adaptive quantum state tomography using active learning. Based on an initial, small set of measurements, the active learning algorithm iteratively proposes the basis configurations which will yield the maximum information gain. We apply this scheme to different quantum states and show an improvement in accuracy over random basis configurations.


Annabelle Bohrdt is a theoretical physicist aiming for a microscopic understanding of strongly correlated and highly entangled quantum states by developing radically new analysis tools. In her research, she combines state-of-the art numerical methods established in condensed matter research, intuitive physical pictures, close collaboration with quantum simulation experiments, and artificial intelligence techniques. She is currently an independent ITAMP postdoctoral fellow at Harvard University associated with the groups of Markus Greiner, Mikhail Lukin, and Ashvin Vishwanath. During her PhD, Annabelle spent two years as an exchange student in the group of Eugene Demler at Harvard, funded by a fellowship from the German National Academic Foundation. She obtained her doctoral degree from Technical University Munich (Germany) and is a finalist for the 2022 APS Deborah Jin thesis award. 

Electrical and Computer Engineering