{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "University of Stuttgart",
    "University of Stuttgart, Institute for Systems Theory and Automatic Control and Center for Integrated Quantum Science and Technology (IQST)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Eya Guizani",
      "orcid": "0009-0009-9204-6236"
    },
    {
      "affs": [
        1
      ],
      "name": "Julian Berberich",
      "orcid": "0000-0001-6366-6238"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9zm6j4wcxsyx8.bib",
  "cited_by": 0,
  "doi": "10.1088/1367-2630/aeb8b0",
  "fulltext_url": null,
  "id": "9zm6j4wcxsyx8",
  "issns": [
    "1367-2630"
  ],
  "landing_url": "https://iopscience.iop.org/article/10.1088/1367-2630/aeb8b0",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "feed-journal",
      "physics/general"
    ],
    [
      "openalex",
      "2207|Advanced Control Systems Optimization"
    ]
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  "openalex": "W4414763011",
  "pdf_url": "https://iopscience.iop.org/article/10.1088/1367-2630/aeb8b0/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "IOP Publishing",
  "ris": "https://research.draconitic.com/paper/9zm6j4wcxsyx8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.1088/1367-2630/aeb8b0",
      "source": "feed-iop",
      "url": "https://iopscience.iop.org/article/10.1088/1367-2630/aeb8b0"
    },
    {
      "first_seen": "2026-10-30T12:17:56Z",
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      "url": "https://iopscience.iop.org/article/10.1088/1367-2630/aeb8b0"
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  "subjects": [
    {
      "field": "physics",
      "origin": "journal-openalex:3107|Quantum Mechanics and Applications",
      "subfield": "amo"
    }
  ],
  "title": "Model predictive quantum control: a modular approach for efficient and robust quantum optimal control",
  "title_text": "Model predictive quantum control: a modular approach for efficient and robust quantum optimal control",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:32Z",
  "url": "https://research.draconitic.com/paper/9zm6j4wcxsyx8",
  "venue": "New Journal of Physics",
  "versions": []
}