{
  "abstract": "Precise measurements of single top-quark production in the associated $tW$-channel require efficient discrimination against the dominant $t\\bar{t}$ background. In this work, we investigate the applicability of quantum machine learning to this classification problem using the Variational Quantum Classifier (VQC) algorithm. Simulated proton-proton collision events from CMS Open Data corresponding to Run-2 are employed to train and evaluate the VQC model. Its performance is compared with two established classical approaches, Boosted Decision Trees (BDTs) and Multi-Layer Perceptron (MLP) using both machine learning metrics and physics-motivated metrics that describe the Signal Significance as a function of Signal Efficiency. The comparison is performed separately for the dileptonic $e-μ$ channel and for the resolved and boosted hadronic topologies. In addition, signal-enriched and background-enriched phase-space regions are studied to assess the dependence of classifier performance on event topology. Our results provide a systematic benchmark for the application of VQC for a realistic High Energy Physics classification task and identify the analysis regimes wherein quantum-inspired models offer competitive discrimination with respect to established classical techniques.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.22504",
  "author_total": 2,
  "authors": [
    {
      "name": "Rohan Hindocha"
    },
    {
      "name": "Priyanka"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2rtb6fzm964n6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.22504",
  "fulltext_url": null,
  "id": "2rtb6fzm964n6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.22504",
  "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",
      "hep-ex"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7222975557",
  "pdf_url": "https://arxiv.org/pdf/2610.22504",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2rtb6fzm964n6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2610.22504",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.22504"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "A comparative study of variational quantum classifiers and classical machine learning for $tW-t\\bar{t}$ discrimination at the LHC",
  "title_text": "A comparative study of variational quantum classifiers and classical machine learning for tW−tt̅ discrimination at the LHC",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/2rtb6fzm964n6",
  "venue": "arXiv",
  "versions": []
}