{
  "abstract": "Nitrogen-vacancy (NV) centers in diamond have attracted considerable attention for micro- and nanoscale thermometry owing to their high precision and spatial resolution. However, achieving sufficient electron spin polarization and signal-to-noise ratio (SNR) typically requires high-power or long-duration laser excitation, which introduces heating and degrades measurement accuracy and stability. To address this trade-off, a reinforcement learning (RL)-based approach is proposed to optimize spin polarization under low-heating constraints. First, a physical model integrating five-level optical pumping rates and NV spin polarization dynamics is established. A temperature-rise constraint model is then developed based on pulsed optically detected magnetic resonance (P-ODMR) measurements under varying laser powers and pulse widths in an external magnetic field. Subsequently, a Progressive Action-Space Q-learning (PAS-Q) algorithm is employed to explore the parameter space and identify optimal laser power and pulse-width combinations. The agent is trained through numerical simulations and the resulting strategy is implemented in a hardware system. Experimental results demonstrate a 44% reduction in laser-induced heating, a 33% increase in spin polarization rate based on T1 relaxation analysis, and a 71% reduction in optimization time compared with traditional methods. This approach enables efficient spin polarization with reduced laser heating and improved optimization efficiency, and may be extended to other heat-sensitive quantum spin sensors, including silicon-vacancy centers in diamond and divacancy defects in silicon carbide.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.04588",
  "author_total": 4,
  "authors": [
    {
      "name": "Yifan Wang"
    },
    {
      "name": "Li Xing"
    },
    {
      "name": "Ruigang Wang"
    },
    {
      "name": "Zheng Qian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/amd59kb42awkn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.04588",
  "fulltext_url": null,
  "id": "amd59kb42awkn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.04588",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.04588",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/amd59kb42awkn.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.04588",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.04588"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Adaptive Self-Optimization of Polarization Efficiency for Low-Power NV Center Thermometers Based on PAS-Q",
  "title_text": "Adaptive Self-Optimization of Polarization Efficiency for Low-Power NV Center Thermometers Based on PAS-Q",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:25Z",
  "url": "https://research.draconitic.com/paper/amd59kb42awkn",
  "venue": "arXiv",
  "versions": []
}