{
  "abstract": "Practitioners increasingly rely on hosted simulation environments, but their performance characteristics remain poorly documented. We present a systematic benchmarking study of GPU-accelerated approximate quantum simulation across two widely used methods: matrix product states (MPS) and Pauli path simulation (PPS), comparing BlueQubit (a hosted tool that handles hardware provisioning, simulator configuration, and job orchestration) against AWS Braket, Quantum Rings, Qiskit pauli-prop, and PauliPropagation (written in Julia). For MPS, we find that GPU runtime yields sub-quadratic scaling with bond dimension, with a growing advantage over CPU at increasing scale. For Pauli path simulation on IBM's 127-qubit kicked Ising benchmark, GPUs deliver up to ${\\sim}1{,}700\\times$ speedup at fine truncation thresholds ($δ= 2.5 \\times 10^{-5}$, 27.6M Pauli terms), and are the only backends that reach accuracy regimes below $δ= 10^{-5}$, which remained inaccessible to the commodity CPU-based implementations and self-contained SDKs evaluated here. We also provide a reproducible characterization of these simulators across regimes, including tradeoffs that isolated evaluations do not show. All benchmarking code and configurations are in a public GitHub repository.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.09882",
  "author_total": 6,
  "authors": [
    {
      "name": "Arul Rhik Mazumder"
    },
    {
      "name": "Hovnatan Karapetyan"
    },
    {
      "name": "Mohammed Zuhair Mullath"
    },
    {
      "name": "Rudy H. Tanin"
    },
    {
      "name": "Hrant Gharibyan"
    },
    {
      "name": "Hayk Tepanyan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6k54dfqvth1x2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.09882",
  "fulltext_url": null,
  "id": "6k54dfqvth1x2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.09882",
  "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.DC"
    ],
    [
      "arxiv",
      "cs.PF"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7168259244",
  "pdf_url": "https://arxiv.org/pdf/2607.09882",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6k54dfqvth1x2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2607.09882",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.09882"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Benchmarking Zero-Setup Quantum Circuit Simulators",
  "title_text": "Benchmarking Zero-Setup Quantum Circuit Simulators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/6k54dfqvth1x2",
  "venue": "arXiv",
  "versions": []
}