{
  "abstract": "Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era and have attracted increasing attention in quantum machine learning. However, most existing QGAN methods rely on patch-based decomposition strategies, which weaken the global consistency of generated images and increase quantum resource overhead. In this work, we investigate a simpler approach: pixel-level, end-to-end image generation using a single-quantum-circuit QGAN. By analyzing the structural matching relationship between the quantum prior and the target data distribution in Hilbert space, we provide a new theoretical perspective for understanding the training behavior of naive end-to-end QGANs. Specifically, we introduce the Quantum Fidelity Landscape (QFL), defined as the pairwise-fidelity structure induced by an ensemble of quantum states and preserved under shared unitary transformations of the quantum generation process. We show that, under a fixed Lipschitz readout, this invariant imposes a one-sided bound on decoded sample separation, motivating calibration of the prior-induced QFL before adversarial training. To validate this theoretical insight, we propose BasicQGAN, a QGAN framework incorporating quantum prior calibration. Before adversarial optimization, BasicQGAN aligns the prior-induced QFL with the data-induced QFL. Experimental results on small-scale grayscale image datasets show that BasicQGAN achieves stable and effective end-to-end pixel-level image generation while requiring fewer qubits and trainable parameters than representative patch-based quantum generators. Furthermore, experiments with different initial quantum-state ensembles show that QFL-calibrated ensembles achieve better generative performance.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36702",
  "author_total": 8,
  "authors": [
    {
      "name": "Xue Yang"
    },
    {
      "name": "Rigui Zhou"
    },
    {
      "name": "Dax Enshan Koh"
    },
    {
      "name": "Siong Thye Goh"
    },
    {
      "name": "Yitao Tang"
    },
    {
      "name": "ShiZheng Jia"
    },
    {
      "name": "Young-Wook Cho"
    },
    {
      "name": "Hongyu Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dpbh5m0v4ck65.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36702",
  "fulltext_url": null,
  "id": "dpbh5m0v4ck65",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36702",
  "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.CV"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7214915176",
  "pdf_url": "https://arxiv.org/pdf/2609.36702",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dpbh5m0v4ck65.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36702",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36702"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation",
  "title_text": "Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/dpbh5m0v4ck65",
  "venue": "arXiv",
  "versions": []
}