{
  "abstract": "In quantum computing, the readout always carries finite-shot sampling noise. Suppressing this noise through repeated measurements raises the cost of a quantum neural network (QNN). Meanwhile, a stochastic diffusion sampler deliberately adds randomness at each reverse (generation) step. This paper turns the measurement noise from a liability into a resource. Specifically, we use the noise quantum measurement produces anyway to replace part of the noise the sampler would otherwise inject. We characterize the measurement error as multinomial sampling noise propagated through a trainable linear readout. To connect the circuit's Born probabilities to the denoising task, we further formulate the diffusion process on a bounded zero-sum hyperplane mapped to the probability simplex. Therefore, we can employ a compensation noise to match the mean and covariance of the shot-noise-free linear update under some minimum shot conditions. Solving this condition for the shot count gives a state-dependent adaptive budget. For the denoising diffusion implicit model (DDIM), this budget increases as the sampler becomes more deterministic. On an eight-dimensional Gaussian mixture, the schedule achieves comparable generation quality with $303$ shots per sample against a high-shot reference of $1.6\\times10^{6}$, about $5300\\times$ fewer. Latent diffusion experiments on MNIST further validate the proposed mechanism for generation in a learned latent space.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41721",
  "author_total": 7,
  "authors": [
    {
      "name": "Tianhao Guo"
    },
    {
      "name": "Dianxin Zheng"
    },
    {
      "name": "Yuhua Qian"
    },
    {
      "name": "Yaohui Zheng"
    },
    {
      "name": "Yuzhi Yang"
    },
    {
      "name": "Merouane Debbah"
    },
    {
      "name": "Chau Yuen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bc3wmkvm1s9kt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41721",
  "fulltext_url": null,
  "id": "bc3wmkvm1s9kt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41721",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41721",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bc3wmkvm1s9kt.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41721",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41721"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Leveraging Quantum Measurement Randomness for Shot-Efficient Diffusion Models",
  "title_text": "Leveraging Quantum Measurement Randomness for Shot-Efficient Diffusion Models",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:51Z",
  "url": "https://research.draconitic.com/paper/bc3wmkvm1s9kt",
  "venue": "arXiv",
  "versions": []
}