{
  "abstract": "Resource constraints and imperfect quantum operations remain major challenges for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices, particularly for fault-diagnosis tasks involving imbalanced and heterogeneous data. This work proposes QTN-RQNN, a Quantum Tensor Network-Based Recurrent Quantum Neural Network that integrates tensor-network-inspired nearest-neighbor interactions with recurrent quantum processing. The model sequentially encodes input features while propagating quantum memory across recurrent steps and incorporates QSMOTE for class balancing. QTN-RQNN is evaluated on four heterogeneous fault-diagnosis datasets SPID, CWRUBD, EFDD, and SFDD and compared with QNN, QTN-QNN, and RQNN baselines through classification, ablation, multi-seed, convergence, and quantum-resource analyses. Results show dataset-dependent predictive behavior, indicating that increased quantum architectural complexity does not universally improve classification. On SPID, QTN-RQNN improves the RQNN F1-score from 0.3077 to 0.4516 and ROC-AUC from 0.4485 to 0.6970, while the remaining datasets exhibit different performance-resource trade-offs and sensitivity to recurrent timesteps and circuit depth. Across all datasets, QTN-RQNN reduces the two-qubit-gate count from 32 to 24 per sample relative to RQNN, corresponding to a 25% reduction without increasing the four-qubit requirement; the same relative reduction is obtained for QTN-QNN over QNN. These findings establish QTN-RQNN as a resource-conscious recurrent quantum architecture and highlight the need to jointly consider predictive performance and circuit-resource requirements for near-term quantum fault diagnosis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13694",
  "author_total": 6,
  "authors": [
    {
      "name": "Amit S. Patel"
    },
    {
      "name": "Himanshu R. Patel"
    },
    {
      "name": "Bikash K. Behera"
    },
    {
      "name": "Pranav Mehta"
    },
    {
      "name": "Ghanshyam Tejani"
    },
    {
      "name": "Deepak Parashar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9b3mcn7hv11c7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13694",
  "fulltext_url": null,
  "id": "9b3mcn7hv11c7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13694",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7221551234",
  "pdf_url": "https://arxiv.org/pdf/2610.13694",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9b3mcn7hv11c7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:36Z",
      "native_id": "2610.13694",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13694"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "QTN-RQNN: Quantum Tensor Network-Based Recurrent Quantum Neural Network",
  "title_text": "QTN-RQNN: Quantum Tensor Network-Based Recurrent Quantum Neural Network",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:36Z",
  "url": "https://research.draconitic.com/paper/9b3mcn7hv11c7",
  "venue": "arXiv",
  "versions": []
}