{
  "abstract": "Contemporary quantum computing platforms remain, in essence, programmable physical systems whose control is typically mediated through unitary gate abstractions. While such abstractions provide a uniform interface, they obscure important aspects of the underlying hardware and may limit the exploitation of its full capabilities. Direct operation at the control-pulse level offers a more expressive and physically faithful paradigm, enabling, for instance, the implementation of tailored error-mitigation and optimisation strategies. However, this increased expressivity comes at the cost of greater quantum software development complexity, necessitating structured and accessible tooling. We present a software framework, integrated within the QML-Essentials package, that extends quantum machine learning (QML) methodologies to encompass pulse-level modelling. By embedding quantum optimal control techniques within a QML setting, our approach enables the seamless combination of gate-based and pulse-level representations. The framework provides a comprehensive suite of modelling and analytical capabilities. In particular, we introduce composable ansatz constructions based on interchangeable building blocks, and support for end-to-end optimisation of pulse parameters. Motivated by the central role of quantum Fourier models, we further incorporate a range of Fourier-analytic diagnostics, complemented by extended measures of entanglement. All performance-critical components are implemented in a high-performance environment using JAX and supported by a dedicated quantum simulator. Taken together, the framework facilitates reproducible and systematic investigations, while bridging the conceptual and practical divide between abstract circuit models and hardware-aware optimisation. It provides a robust foundation for future developments at the intersection of QML and quantum control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Proceedings of the 2026 IEEE International Conference on Quantum Software (QSW)",
  "arxiv": "2605.21286",
  "author_total": 8,
  "authors": [
    {
      "name": "Maja Franz"
    },
    {
      "name": "Melvin Strobl"
    },
    {
      "name": "Jonathan Hunz"
    },
    {
      "name": "Lukas Scheller"
    },
    {
      "name": "Lucas van der Horst"
    },
    {
      "name": "Eileen Kuehn"
    },
    {
      "name": "Achim Streit"
    },
    {
      "name": "Wolfgang Mauerer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0hkakkgq576x3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.21286",
  "fulltext_url": null,
  "id": "0hkakkgq576x3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.21286",
  "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",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.21286",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0hkakkgq576x3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2605.21286",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.21286"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Software Between Quantum and Machine Learning -- And Down to Pulses",
  "title_text": "Software Between Quantum and Machine Learning -- And Down to Pulses",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/0hkakkgq576x3",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1109/qsw72780.2026.00017",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}