{
  "abstract": "We introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs, with a focus on data-parallel tableau evolution and scalable handling of projective measurements. Our approach reformulates key bottlenecks in stabilizer simulation – such as Gaussian elimination and measurement updates – into GPU-tailored primitives that eliminate sequential dependencies and maximize memory coalescing. We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator designed for large qubit counts and many-shot sampling. Across a broad benchmark suite reaching 180,000 qubits and depth 1,000 (roughly 130M gates), QuaSARQ shows substantial runtime improvements, with up to 105 × speedup, and over 80% energy reduction on demanding instances. Moreover, QuaSARQ consistently outperforms Stim, a state-of-the-art CPU-optimized stabilizer simulator, as well as Qiskit-Aer (CPU/GPU), Qibo, Cirq, and PennyLane. Finally, QuaSARQ exhibits a significant advantage in many-shot sampling on large workloads. These results demonstrate that our parallel algorithms can significantly advance the scalability of stabilizer-circuit simulation, particularly for workloads involving extensive measurements and sampling.",
  "abstract_shown": true,
  "affiliations": [
    "Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammad Osama",
      "orcid": "0000-0002-5023-5348"
    },
    {
      "affs": [
        0
      ],
      "name": "Dimitrios Thanos",
      "orcid": "0000-0002-0719-1036"
    },
    {
      "affs": [
        0
      ],
      "name": "Alfons Laarman",
      "orcid": "0000-0002-2433-4174"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7f51snhngtzqj.bib",
  "cited_by": 0,
  "doi": "10.22331/q-2026-10-01-2225",
  "fulltext_url": null,
  "id": "7f51snhngtzqj",
  "issns": [
    "2521-327X"
  ],
  "landing_url": "https://quantum-journal.org/papers/q-2026-10-01-2225/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7137974349",
  "pdf_url": "https://quantum-journal.org/papers/q-2026-10-01-2225/pdf/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften",
  "ris": "https://research.draconitic.com/paper/7f51snhngtzqj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:58Z",
      "native_id": "10.22331/q-2026-10-01-2225",
      "source": "crossref",
      "url": "https://quantum-journal.org/papers/q-2026-10-01-2225/"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "issn:2521-327X",
      "subfield": "quantum"
    }
  ],
  "title": "GPU-Accelerated Quantum Simulation of Stabilizer Circuits",
  "title_text": "GPU-Accelerated Quantum Simulation of Stabilizer Circuits",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:58Z",
  "url": "https://research.draconitic.com/paper/7f51snhngtzqj",
  "venue": "Quantum",
  "versions": []
}