{
  "abstract": "Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce $\\textit{Backline}$, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of $2.305~μ$s to an AMD Ryzen Threadripper PRO CPU and $4.5~μ$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09270",
  "author_total": 13,
  "authors": [
    {
      "name": "Joseph K. L. Lee"
    },
    {
      "name": "Mehrdad Malekmohammadi"
    },
    {
      "name": "Hong-Sheng Zheng"
    },
    {
      "name": "Shuli Shu"
    },
    {
      "name": "Cheick Doumbia"
    },
    {
      "name": "Kalman Szenes"
    },
    {
      "name": "Mehran Zamani Abnili"
    },
    {
      "name": "Thomas Ainsworth"
    },
    {
      "name": "Matthew Seymour"
    },
    {
      "name": "Thomas Germain"
    },
    {
      "name": "Leonhard Neuhaus"
    },
    {
      "name": "Josh Izaac"
    },
    {
      "name": "Lee J. O'Riordan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3cpb9y8c488xp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09270",
  "fulltext_url": null,
  "id": "3cpb9y8c488xp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09270",
  "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.DC"
    ],
    [
      "arxiv",
      "cs.PL"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7212108122",
  "pdf_url": "https://arxiv.org/pdf/2609.09270",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3cpb9y8c488xp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2609.09270",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09270"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs",
  "title_text": "Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/3cpb9y8c488xp",
  "venue": "arXiv",
  "versions": []
}