Blatz, Tizian and Kwan, Joyce and Leonard, Julian and Bohrdt, Annabelle (2024) Bayesian Optimization for Robust State Preparation in Quantum Many-Body Systems. QUANTUM, 8: 1388. ISSN 2521-327X
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New generations of ultracold-atom experiments are continually raising the demand for efficient solutions to optimal control problems. Here, we apply Bayesian optimization to improve a state-preparation protocol recently implemented in an ultracold-atom system 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 10 x faster 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SIMULATIONS; ELECTRONS; MODEL |
| Subjects: | 500 Science > 530 Physics |
| Divisions: | Physics > Institute of Theroretical Physics > Chair Professor Grifoni > Group Milena Grifoni |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 Dec 2025 06:34 |
| Last Modified: | 10 Dec 2025 06:34 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65139 |
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