{
  "abstract": "Quantum applications increasingly execute as multi-stage quantum-classical pipelines, interleaving QPU computation with classical stages like circuit generation, transpilation, layout mapping, quantum error mitigation (QEM), and post-processing. These stages have diverse resource requirements and exhibit stochastic behavior under drifting hardware noises, yet existing workflow frameworks treat them as static, isolated components. We present LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines. LLQM decomposes pipelines into fine-grained tasks and continuously profiles their CPU/GPU, memory, QPU, and queue dependencies alongside real-time hardware states. This unified runtime abstraction captures cross-stage resource dependencies and reveals how classical and quantum decisions interact, enabling characterization of their impact on fidelity and resource consumption. We evaluate LLQM using QEM as a representative pipeline stage, on IBM 156-qubit Heron r2 processors with circuits up to 100 qubits and 1e7 transpiled gates. Our results show that continuous profiling exposes runtime bottlenecks and enables hardware-, fidelity-, and workload-aware optimizations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05814",
  "author_total": 7,
  "authors": [
    {
      "name": "Ayush Bansal"
    },
    {
      "name": "Owen Cochell"
    },
    {
      "name": "Santiago Núñez-Corrales"
    },
    {
      "name": "Marcos Frenkel"
    },
    {
      "name": "Seetharami Seelam"
    },
    {
      "name": "Apoorve Mohan"
    },
    {
      "name": "Tianyin Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/at8r4amwpmcca.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05814",
  "fulltext_url": null,
  "id": "at8r4amwpmcca",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05814",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.ET"
    ],
    [
      "arxiv",
      "cs.OS"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7212035146",
  "pdf_url": "https://arxiv.org/pdf/2609.05814",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/at8r4amwpmcca.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2609.05814",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05814"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines",
  "title_text": "Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/at8r4amwpmcca",
  "venue": "arXiv",
  "versions": []
}