{
  "abstract": "Deploying quantum neural networks (QNNs) on sparse hardware requires preserving task accuracy while reducing compiler-induced two-qubit gates, SWAPs, and depth. We present ConQUESTA (\\underline{C}onstrained \\underline{Qu}antum \\underline{E}ntangler \\underline{S}election with \\underline{T}opology \\underline{A}wareness), a post training pruning framework that combines task saliency with compiler cost, screens candidate entanglers, and searches complete pruning masks under an explicit accuracy constraint. Unlike one-shot ranking, each mask is transpiled as a whole across multiple coupling topologies. Across 4-, 6-, and 8-qubit QNNs and eight model seeds at 55% pruning, ConQUESTA achieves mean resource reductions of 61.6% on Breast Cancer, 72.0% on Digits01, and 82.2% on Iris, exceeding the strongest matched simple baseline by 3.5, 1.7, and 8.9 percentage points, respectively. Fixed-mask recovery raises feasibility from 16/24 to 22/24 runs. In a fixed-k=4 scaling study on a scaling probe of 4 to 12-qubits, the raw mask space grew from 495 to 12.1M while the evaluated masks increase only from 189 to 305. Resource-weight changes alter depth/routing priorities with at most a 0.44-point mean validation shift.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.23415",
  "author_total": 2,
  "authors": [
    {
      "name": "Saim Rehman"
    },
    {
      "name": "Muhammad Shafique"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eqwk97g6svqkx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.23415",
  "fulltext_url": null,
  "id": "eqwk97g6svqkx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.23415",
  "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.AR"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7222916311",
  "pdf_url": "https://arxiv.org/pdf/2610.23415",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/eqwk97g6svqkx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2610.23415",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.23415"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "ConQUESTA: Topology-Aware Post-Training Structural Pruning of Quantum Neural Networks",
  "title_text": "ConQUESTA: Topology-Aware Post-Training Structural Pruning of Quantum Neural Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/eqwk97g6svqkx",
  "venue": "arXiv",
  "versions": []
}