{
  "abstract": "The use of reinforcement learning (RL) in the design of quantum control strategies for pulse level control of NISQ era quantum devices is attracting growing interest. Model-free RL, not requiring preexisting knowledge of the physical system, acts as a black-box optimization tool well suited for noisy devices. In this work, we provide a simulation-based analysis of the performance of model-free RL for state preparation and qubit gate design in a challenging setup: indirect control of NV nuclear spins in a spin-bath. In particular, the spin-bath is modeled via the generalized cluster correlation expansion accounting for magnetic impurities and intra-bath interactions. We analyze the performance of the RL agents by performing simulations with minimally observable environments and noisy, sparse rewards as introduced in (Phys. Rev. X 12, 011059), and compare them to other state-of-the-art black-box optimization tools.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21987",
  "author_total": 6,
  "authors": [
    {
      "name": "Dennis Herb"
    },
    {
      "name": "Mirko Rossini"
    },
    {
      "name": "Sabine Wölk"
    },
    {
      "name": "Joachim Ankerhold"
    },
    {
      "name": "Dominik Maile"
    },
    {
      "name": "Alessio Belenchia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/49s86z83g0h6j.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21987",
  "fulltext_url": null,
  "id": "49s86z83g0h6j",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21987",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7222741835",
  "pdf_url": "https://arxiv.org/pdf/2610.21987",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/49s86z83g0h6j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:50Z",
      "native_id": "2610.21987",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21987"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum control of solid state qubits via Reinforcement Learning",
  "title_text": "Quantum control of solid state qubits via Reinforcement Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:50Z",
  "url": "https://research.draconitic.com/paper/49s86z83g0h6j",
  "venue": "arXiv",
  "versions": []
}