Bayesian Optimization for Robust State Preparation in Quantum Many-Body Systems

New generations of ultracold-atom experiments are continually raising the demand for efficient solutions to optimal control problems. Here, we present recent progress [1] in applying Bayesian optimization to improve state-preparation protocols for fractional quantum Hall states, such as a two-particle Laughlin state recently realized in an ultracold-atom quantum simulator [2]. Compared to manual ramp design, we demonstrate the superior performance of our optimization approach in a numerical simulation – resulting in a protocol that is 10x faster at equal 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. We find that numerical simulation can be expected to substantially reduce the number of experiments that need to be performed using transfer learning techniques. The proposed protocol can guide the design of both diabatic and adiabatic preparation schemes and pave the way toward the realization of more complex many-body quantum states in experiments.

[1] T. Blatz, J. Kwan, J. Léonard, A. Bohrdt, Quantum 8, 1388 (2024)

[2] J. Léonard, S. Kim, J. Kwan, P. Segura, F. Grusdt, C. Repellin, N. Goldman, M. Greiner, Nature 619, 495-499 (2023)