{
  "abstract": "Many quantum algorithms for classically difficult optimization tasks must return high-quality bitstrings from finitely many circuit executions, whereas most quantum error-mitigation methods target expectation values. We study sample-level recovery when measured probability mass is distributed around multiple latent bitstrings, called centers. Each component of the measured probability mass is called a source and we assume that each center is associated with one source. We identify dominance-at every coordinate, more than half of a retained region's probability mass comes from one source and agrees with its center-as a sufficient condition under which majority voting recovers that center with exponentially decreasing error probability. We show that nearest-center assignment, as used in clustering algorithms such as the $k$-modes algorithm, can fail to produce dominated regions even when the true centers are known. This failure motivates responsibility thresholding and a local dominance screen, whose combination we call dominance-aware (DA) refinement. Synthetic and simulated MaxCut-QAOA experiments show that DA refinement favors precision, while $k$-modes with DA refinement improves overall center recovery. All procedures are classical post-processing and require no additional quantum-circuit executions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01744",
  "author_total": 3,
  "authors": [
    {
      "name": "Mohsen Ghodrati"
    },
    {
      "name": "Kausthubh Chandramouli"
    },
    {
      "name": "Dror Baron"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0c1rejcmbfwkt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01744",
  "fulltext_url": null,
  "id": "0c1rejcmbfwkt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01744",
  "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.IT"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7207841939",
  "pdf_url": "https://arxiv.org/pdf/2609.01744",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0c1rejcmbfwkt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.01744",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01744"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering",
  "title_text": "Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:20Z",
  "url": "https://research.draconitic.com/paper/0c1rejcmbfwkt",
  "venue": "arXiv",
  "versions": []
}