{
  "abstract": "Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is constrained by long-tail predicate imbalance. Classical SGG models rely on dataset statistics, leading to biased predictions toward frequent relations rather than fine-grained semantic predicates. Although debiasing improves mean recall, predicate classification in existing frameworks still relies on high-dimensional classical decision modules with high parameter overhead. This work introduces a resource-efficient hybrid quantum predicate classifier for SGG by replacing the classical predicate head in the CFEN with a QP-Head trained using class-balanced WCE. To the best of our knowledge, this work is among the first evaluations of a hybrid quantum architecture for scene graph predicate classification on Visual Genome 150. We evaluate the impact of qubit count, encoding strategy, entangling topology, and circuit depth. Under a 4-qubit configuration using Amplitude Embedding and Strongly Entangling Layers, the QP-Head projects 4096-dimensional pair features into a 16-dimensional quantum input while using 96 trainable quantum parameters. Operating within this compressed representation space under WCE training, it achieves strong long-tail recognition, reaching a peak mR@100 of 57.25% compared to the 41.1% mR@100 reported in the CFEN baseline under standard training. Protocol-matched ablations indicate that the primary advantage of the quantum head lies in extreme representational compactness rather than an accuracy gain over full-dimensional weighted classical heads: it achieves global recall within 0.19pp of the 2048-dimensional WCE classical baseline while operating from a 128x smaller head feature space. Scaling to 8 qubits maintains robust long-tail performance (55.38% mR@100 with 384 quantum parameters), while depth analysis highlights expressibility-latency trade-offs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.04689",
  "author_total": 3,
  "authors": [
    {
      "name": "Prerana Ramkumar"
    },
    {
      "name": "Nouhaila Innan"
    },
    {
      "name": "Muhammad Shafique"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8pyzd83r2pver.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.04689",
  "fulltext_url": null,
  "id": "8pyzd83r2pver",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.04689",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1707|Multimodal Machine Learning Applications"
    ]
  ],
  "openalex": "W7163521107",
  "pdf_url": "https://arxiv.org/pdf/2606.04689",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8pyzd83r2pver.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2606.04689",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.04689"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation",
  "title_text": "QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/8pyzd83r2pver",
  "venue": "arXiv",
  "versions": []
}