{
  "abstract": "Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/quantum feature inspection. The source code is publicly available at https://github.com/Dilli822/ SimAM-HVQC",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28491",
  "author_total": 1,
  "authors": [
    {
      "name": "Dilli Hang Rai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7wqat5vxkscmc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28491",
  "fulltext_url": null,
  "id": "7wqat5vxkscmc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28491",
  "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.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7214309571",
  "pdf_url": "https://arxiv.org/pdf/2609.28491",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7wqat5vxkscmc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2609.28491",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28491"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment",
  "title_text": "Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/7wqat5vxkscmc",
  "venue": "arXiv",
  "versions": []
}