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Simulation-Based Quantum System Inference with Neural Posterior Estimation — arXiv:2609.34995

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.34995
License (as archived): CC0-1.0
Archived: 2026-10-07T22:41:02.992Z
SHA-256 of the archived text: 6df8ef48aa549bb4ce9aed3c419593dd2076d4f67e199d7a576b8d50666e37ba
Stable URL of this page: https://metraipolis.com/documento/dc_503151ab06dd

How to cite

Simulation-Based Quantum System Inference with Neural Posterior Estimation — arXiv:2609.34995 — arXiv (en), https://arxiv.org/abs/2609.34995 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_503151ab06dd (Metraipolis archive dc_503151ab06dd, sha256 6df8ef48aa549bb4ce9aed3c419593dd2076d4f67e199d7a576b8d50666e37ba)

BibTeX

@misc{dc503151ab06dd,
  title = {Simulation-Based Quantum System Inference with Neural Posterior Estimation — arXiv:2609.34995},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.34995},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_503151ab06dd; sha256 6df8ef48aa549bb4ce9aed3c419593dd2076d4f67e199d7a576b8d50666e37ba},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.34995. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Quantum Physics (quant-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.34995 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:14:28.885Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).