{
  "abstract": "Rydberg-atom sensors have emerged as a promising quantum technology for broadband radio-frequency detection owing to their intrinsic calibration, wide frequency coverage, and high sensitivity. However, their potential for intelligent identification of real-world wireless signals remains largely unexplored. Here, we demonstrate a quantum-enabled framework for mobile signal classification based on Rydberg electromagnetically induced transparency spectroscopy and deep learning. Three types of wireless emissions, including cellular voice call, Wi-Fi, and 5G signals from three commercial smartphone brands, are measured to construct a spectral-fingerprint dataset. A convolutional neural network trained on this dataset achieves an initial baseline classification accuracy of 93.44\\%. To compensate for spectral drifts induced by temporal and environmental variations, a progressive fine-tuning strategy is employed to continuously adapt the pretrained model, enabling robust classification across diverse deployment scenarios. Furthermore, continuous real-time monitoring is demonstrated under dynamically varying wireless conditions, including cellular voice call, Wi-Fi, and their coexistence. The proposed system achieves an overall frame-level recognition accuracy of 87.67\\% over a 60-s measurement. These results establish a pathway toward intelligent quantum radio-frequency sensing and highlight the potential of Rydberg-atom platforms for real-time wireless signal awareness and spectrum monitoring.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.35301",
  "author_total": 10,
  "authors": [
    {
      "name": "Yi-Ming Yin"
    },
    {
      "name": "Ming-Yao Cui"
    },
    {
      "name": "Ling-Yu Du"
    },
    {
      "name": "Jin-Hang Fang"
    },
    {
      "name": "Luan-Yi Wei"
    },
    {
      "name": "Bang Liu"
    },
    {
      "name": "Li-Hua Zhang"
    },
    {
      "name": "Kai Wang"
    },
    {
      "name": "Kai-Bin Huang"
    },
    {
      "name": "Dong-Sheng Ding"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1j54yqn5fxm8m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.35301",
  "fulltext_url": null,
  "id": "1j54yqn5fxm8m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.35301",
  "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",
      "1702|Wireless Signal Modulation Classification"
    ]
  ],
  "openalex": "W7223961655",
  "pdf_url": "https://arxiv.org/pdf/2610.35301",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1j54yqn5fxm8m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.35301",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.35301"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Spectral fingerprint recognition of mobile signals via Rydberg atomic sensors",
  "title_text": "Spectral fingerprint recognition of mobile signals via Rydberg atomic sensors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/1j54yqn5fxm8m",
  "venue": "arXiv",
  "versions": []
}