{
  "abstract": "We report on a fundamental disparity between stochastic noise models and algorithmic performance in NISQ-era classifiers. Utilizing the ibm_kingston processor, we characterize the \"Kingston Constant\" ($κ\\approx 0.07$), representing a 93% signal magnitude collapse. Despite this decay, we show via a hardware-calibrated stochastic digital twin that a Hadamard Test based Perceptron maintains a 93.9% accuracy for the MNIST dataset, validating our proposed Hadamard Resilience Effect under affine depolarization. Through parametric simulation, we establish a critical gate-noise threshold at $λ_{\\text{crit}} \\approx 0.65$ (corresponding to a critical signal retention $α_{\\text{crit}} \\approx 0.35$) where topological rank preservation breaks down. Furthermore, physical execution at high feature depths ($N = 256$) reveals a systemic divergence---the ``Coherence Gap'' ($Δρ\\approx 0.91$)---where physical hardware classification accuracy collapses to 53.0\\% due to a ``Coherence Wall'' at a circuit depth ($D \\approx 10\\text{k}$) exceeding the hardware's resilient depth limit ($D_{\\text{max}} \\approx 3.5\\text{k}$). This gap is consistent with coherent phase errors and crosstalk being the dominant unmodeled contributors under the tested hardware configuration, establishing a predictive operational boundary for NISQ classification architectures.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.10638",
  "author_total": 1,
  "authors": [
    {
      "name": "Wladimir Silva"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egr1xmg31dafh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.10638",
  "fulltext_url": null,
  "id": "egr1xmg31dafh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.10638",
  "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",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7160928793",
  "pdf_url": "https://arxiv.org/pdf/2605.10638",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/egr1xmg31dafh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:52Z",
      "native_id": "2605.10638",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.10638"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantifying the Hadamard Resilience Effect and the Coherence Gap in NISQ-Era Classifiers",
  "title_text": "Quantifying the Hadamard Resilience Effect and the Coherence Gap in NISQ-Era Classifiers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:52Z",
  "url": "https://research.draconitic.com/paper/egr1xmg31dafh",
  "venue": "arXiv",
  "versions": []
}