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Peer-reviewedCC BY 4.0New Journal of Physics

Model predictive quantum control: a modular approach for efficient and robust quantum optimal control

  • Eya Guizani1
  • Julian Berberich2
  1. 1University of Stuttgart
  2. 2University of Stuttgart, Institute for Systems Theory and Automatic Control and Center for Integrated Quantum Science and Technology (IQST)
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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.

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Cite

BibTeXRIS
BibTeX
@article{guizani2026model,
  title = {{Model predictive quantum control: a modular approach for efficient and robust quantum optimal control}},
  author = {Eya Guizani and Julian Berberich},
  journal = {New Journal of Physics},
  year = {2026},
  month = oct,
  doi = {10.1088/1367-2630/aeb8b0},
  url = {https://iopscience.iop.org/article/10.1088/1367-2630/aeb8b0},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Model predictive quantum control: a modular approach for efficient and robust quantum optimal control
AU  - Eya Guizani
AU  - Julian Berberich
PY  - 2026
DA  - 2026/10/30
JO  - New Journal of Physics
DO  - 10.1088/1367-2630/aeb8b0
UR  - https://iopscience.iop.org/article/10.1088/1367-2630/aeb8b0
AB  - 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.
SN  - 1367-2630
ER  -