Freiburg 2024 – scientific programme
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Q: Fachverband Quantenoptik und Photonik
Q 67: Machine Learning
Q 67.6: Talk
Friday, March 15, 2024, 16:00–16:15, Aula
Bayesian Optimization for Robust State Preparation in Quantum Many-Body Systems — •Tizian Blatz1,2 and Annabelle Bohrdt2,3 — 1Ludwig-Maximilians-Universität München, München, Germany — 2Munich Center for Quantum Science and Technology (MCQST), München, Germany — 3University of Regensburg, Regensburg, Germany
New generations of ultracold atom experiments are continually raising the demand for efficient solutions to optimal control problems. We present a Bayesian-optimization approach to improve a state- preparation protocol recently implemented in an ultracold-atom experiment to realize a two-particle fractional quantum Hall state. Compared to manual ramp design, we demonstrate the superior performance of our optimization approach in a numerical simulation, resulting in a protocol that is faster by an order of magnitude at the same fidelity, even when taking into account experimentally realistic levels of disorder in the system. We extensively analyze and discuss questions of robustness and the relationship between numerical simulation and experimental realization, and how to make the best use of the surrogate model trained during optimization. We find that numerical simulation can be expected to substantially reduce the number of experiments that need to be performed with even the most basic transfer learning techniques. The proposed protocol and workflow will pave the way toward the realization of more complex many-body quantum states in experiments.
Keywords: Ultracold atoms in optical lattices; Machine learning; Quantum optimal control