Speaker
Description
In this presentation, I will talk about my personal journey in physics – starting in high school and concluding with my research in quantum many-body control. In the first half, I will discuss what motivated me during the different stages of my studies and share a few insights that I learned on the way. In the second half, I will dive deeper into my current field of research: quantum many-body systems (and how to tame and control them). Understanding quantum many-body effects is crucial not only for quantum technologies but also to provide new insights into many promising research directions such as the study of high temperature superconductivity. However, the many-body aspect also presents a computational challenge known as the curse of dimensionality. I will introduce two different strategies of facing this challenge. In a first approach, I will show how to leverage tensor networks to efficiently simulate quantum many-body systems classically. As a second approach, I will discuss how in an experimental setup one quantum system can be used to simulate another. I will explain why optimal control is required to successfully run such a quantum simulation and show how reinforcement learning, a subfield of machine learning, can be used to solve these quantum control tasks. Finally, I will tell you how all of the introduced concepts come together in a general framework for quantum many-body control.
External references
- 24060007
- 776ea591-3857-4fb0-ba57-7fb0ccc72b14
