{
  "abstract": "We benchmark quantum convolutional neural networks (QCNNs) and their classical surrogates for phase differentiation in one-dimensional strongly correlated systems with beyond-nearest-neighbour terms. Using an anisotropic Heisenberg spin chain and an extended $t$-$t'$ Fermi-Hubbard model as representative spin and fermionic testbeds, we train QCNN classifiers on labelled ground-state datasets and map out finite-size phase diagrams. In hybrid setups, we employ Pauli backpropagation to express the QCNN measurement as a weighted sum of Pauli-string expectation values to be evaluated directly on input states, and study how truncation parameters control the trade-off between approximation accuracy and the size of the resulting Pauli expansion. Robust convergence is achieved for the Heisenberg model under aggressive truncation, whereas the Fermi-Hubbard model requires substantially looser thresholds, resulting in a sharp growth in retained Pauli strings and evaluation cost. Our results contribute to charting where hybrid quantum-classical training is viable for learning strongly correlated quantum states, and provide practical guidance for choosing truncation regimes in Pauli backpropagation-based surrogates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.00255",
  "author_total": 6,
  "authors": [
    {
      "name": "Tatiana A. Bespalova"
    },
    {
      "name": "Réka Schwengelbeck"
    },
    {
      "name": "Laura Mismetti"
    },
    {
      "name": "Johannes Schachenmayer"
    },
    {
      "name": "Ivano Tavernelli"
    },
    {
      "name": "Francesco Tacchino"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3rfszhvpztgec.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.00255",
  "fulltext_url": null,
  "id": "3rfszhvpztgec",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.00255",
  "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",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.00255",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3rfszhvpztgec.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.00255",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.00255"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Benchmarking Quantum Convolutional Neural Networks and their classical surrogates on long-range strongly correlated models",
  "title_text": "Benchmarking Quantum Convolutional Neural Networks and their classical surrogates on long-range strongly correlated models",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/3rfszhvpztgec",
  "venue": "arXiv",
  "versions": []
}