{
  "abstract": "Rigorous empirical validation of quantum machine learning on natural language tasks remains scarce. We evaluate a 10-qubit hybrid quantum-classical variational circuit (2,148 parameters) for paraphrase detection across three benchmarks: MRPC, Quora Question Pairs (QQP), and adversarial PAWS. On QQP (n = 10 seeds), the circuit achieves 75.53% +/- 0.75%. accuracy, statistically outperforming parameter-matched classical baselines (DeepMLP: p = 0.015, Cohen's d = 1.20; F1: p < 0.001, d = 2.32) and surpassing DistilBERT-4bit with 31,191 fewer parameters. On MRPC the optimal 2-layer variant reaches 92% of BERT-base accuracy at 54,000 lower parameter cost. Circuit depth analysis reveals a dataset-depth scaling effect; entanglement analysis via the Meyer-Wallach measure identifies multi-qubit entanglement as the primary performance driver (r = 0.85 across four variants). Adversarial evaluation on PAWS reveals emergent robustness: 98.2% recall versus 81.6% classical (+16.6 pp, d = 1.24, p < 0.001), without adversarial training. These results constitute the first systematic parameter-matched multi-benchmark empirical validation of hybrid variational circuits for NLP. All results are from classical simulation; hardware validation is future work.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.14529",
  "author_total": 3,
  "authors": [
    {
      "name": "Farha Nausheen"
    },
    {
      "name": "Khandakar Ahmed"
    },
    {
      "name": "Farina Riaz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8qgrcawgbvy0h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.14529",
  "fulltext_url": null,
  "id": "8qgrcawgbvy0h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.14529",
  "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",
      "cs.CL"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7213327270",
  "pdf_url": "https://arxiv.org/pdf/2609.14529",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8qgrcawgbvy0h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.14529",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.14529"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement",
  "title_text": "Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/8qgrcawgbvy0h",
  "venue": "arXiv",
  "versions": []
}