{"ID":2874279,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05167","arxiv_id":"2509.05167","title":"Model predictive quantum control: A modular approach for efficient and robust quantum optimal control","abstract":"Model predictive control (MPC) is one of the most successful modern control methods. It relies on repeatedly solving a finite-horizon optimal control problem and applying the beginning piece of the optimal input. In this paper, we develop a modular framework for improving efficiency and robustness of quantum optimal control (QOC) via MPC. We first provide a tutorial introduction to basic concepts of MPC from a QOC perspective. We then present multiple MPC schemes, ranging from simple approaches to more sophisticated schemes which admit stability guarantees. This yields a modular framework which can be used 1) to improve efficiency of open-loop QOC and 2) to improve robustness of closed-loop quantum control by incorporating feedback. We demonstrate these benefits with numerical results, where we benchmark the proposed methods against competing approaches.","short_abstract":"Model predictive control (MPC) is one of the most successful modern control methods. It relies on repeatedly solving a finite-horizon optimal control problem and applying the beginning piece of the optimal input. In this paper, we develop a modular framework for improving efficiency and robustness of quantum optimal co...","url_abs":"https://arxiv.org/abs/2509.05167","url_pdf":"https://arxiv.org/pdf/2509.05167v1","authors":"[\"Eya Guizani\",\"Julian Berberich\"]","published":"2025-09-05T15:06:02Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"math.OC\",\"quant-ph\"]","methods":"[]","has_code":false}