{
  "abstract": "Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings. This work adapts the Quantum Leaky Integrate-and-Fire (QLIF) spiking neural network for time-series regression tasks, specifically short-term multivariate weather forecasting. We extend QLIF beyond classification and demonstrate its applicability to continuous-valued prediction problems. The QLIF-CAST model encodes neuron excitation states as single-qubit quantum superpositions, driven by R_x rotation gates and T1 relaxation decay, and is embedded within a hybrid quantum-classical recurrent architecture. We conduct two distinct evaluations. First, a controlled comparison against a parameter-matched classical LIF baseline on a multivariate weather dataset shows that QLIF-CAST achieves 15.4% lower MSE and 4.4% lower MAE, demonstrating that quantum neuronal dynamics reduce prediction error over classical equivalents. Second, a cross-domain comparative analysis with state-of-the-art quantum LSTM (QLSTM) and quantum neural network (QNN) models on air quality and wind speed benchmarks reveals that QLIF-CAST converges in up to 94% less training time, occupying a distinct position in the speed-error trade-off space. Hardware verification on IBM Marrakesh (156-qubit QPU) confirms reliable circuit execution with only 1.2% average deviation from simulation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.18333",
  "author_total": 5,
  "authors": [
    {
      "name": "Alberto Marchisio"
    },
    {
      "name": "Aayan Ebrahim"
    },
    {
      "name": "Nouhaila Innan"
    },
    {
      "name": "Muhammad Kashif"
    },
    {
      "name": "Muhammad Shafique"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4v98tawhvqbem.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.18333",
  "fulltext_url": null,
  "id": "4v98tawhvqbem",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.18333",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7161723640",
  "pdf_url": "https://arxiv.org/pdf/2605.18333",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4v98tawhvqbem.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2605.18333",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.18333"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting",
  "title_text": "QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/4v98tawhvqbem",
  "venue": "arXiv",
  "versions": []
}