Berlin 2024 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

QI: Fachverband Quanteninformation

QI 23: Quantum Control

QI 23.13: Talk

Thursday, March 21, 2024, 12:45–13:00, HFT-FT 131

Improving robustness of quantum feedback control with reinforcement learning — •Manuel Guatto, Francesco Ticozzi und Gian Antonio Susto — Università degli studi di Padova, 35131 Padova, via Gradenigo 6B

Different reinforcement learning techniques are used to derive a feedback law for state preparation of a target state for a test system undergoing varying amounts of noise that is not included in the system model. Comparing the results indicates that the learned controls are more robust to unmodeled perturbations with respect to simple feedback strategy based on optimized population transfer, and that training on simulated nominal model retain the same advantages displayed by controllers trained on real data. The possibility of effective off-line training of robust controllers promises significant advantages towards practical implementation.

Keywords: Reinforcement Learning; Quantum Control; Machine learning