{
  "abstract": "Models of quantum systems faithfully map system parameters to observations, but the inverse problem of parameter inference from measurement data presents a fundamental challenge: computationally intractable likelihoods due to an exponentially large Hilbert space. Here, we introduce simulation-based quantum system inference, a unified, likelihood-free framework that learns parameter posteriors directly from classical simulation data. The central idea is to pair polynomial-cost classical simulators, such as Pauli propagation and tensor networks, with normalizing flows or other neural density estimators for accurate, reusable inference. A single model, trained once, maps any new measurement record to its posterior in one forward pass---turning per-experiment inference into a fixed, up-front cost. We numerically demonstrate the framework's versatility across Pauli noise learning, quantum error mitigation, quantum state tomography, and Hamiltonian learning, with examples involving 81-qubit shallow circuits and 735-parameter inference. In each case, the approach yields accurate estimates of identifiable parameters, while posterior uncertainty provides additional diagnostics of non-identifiability and indicates where further characterization is needed. Our framework reduces data-acquisition requirements in quantum experiments and accelerates parameter inference, providing a practical route to characterizing and improving large-scale quantum systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34995",
  "author_total": 4,
  "authors": [
    {
      "name": "Hang Zou"
    },
    {
      "name": "Anton Frisk Kockum"
    },
    {
      "name": "Martin Rahm"
    },
    {
      "name": "Simon Olsson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/et9tbrrzzsgy1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34995",
  "fulltext_url": null,
  "id": "et9tbrrzzsgy1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34995",
  "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": "W7214762217",
  "pdf_url": "https://arxiv.org/pdf/2609.34995",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/et9tbrrzzsgy1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.34995",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34995"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Simulation-Based Quantum System Inference with Neural Posterior Estimation",
  "title_text": "Simulation-Based Quantum System Inference with Neural Posterior Estimation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/et9tbrrzzsgy1",
  "venue": "arXiv",
  "versions": []
}