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Discovering Models for Experimental Quantum Systems with Agentic AI — arXiv:2611.00620

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00620
License (as archived): CC0-1.0
Archived: 2026-11-03T10:55:40.636Z
SHA-256 of the archived text: 8864b5716b4c74fc408a523970aca0d8af4a692f4e835546102401fb97132d4e
Stable URL of this page: https://metraipolis.com/documento/dc_fa5374fc72ca

How to cite

Discovering Models for Experimental Quantum Systems with Agentic AI — arXiv:2611.00620 — arXiv (en), https://arxiv.org/abs/2611.00620 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fa5374fc72ca (Metraipolis archive dc_fa5374fc72ca, sha256 8864b5716b4c74fc408a523970aca0d8af4a692f4e835546102401fb97132d4e)

BibTeX

@misc{dcfa5374fc72ca,
  title = {Discovering Models for Experimental Quantum Systems with Agentic AI — arXiv:2611.00620},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00620},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fa5374fc72ca; sha256 8864b5716b4c74fc408a523970aca0d8af4a692f4e835546102401fb97132d4e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00620. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A central obstacle in realizing color center cavity-QED-based quantum technologies is inhomogeneity, or disorder, among individual emitters. As the number of emitters increases, so does the complexity of the experimental data, making it difficult to quantify the present physical mechanisms of disorder. Here we introduce an artificial intelligence-driven model discovery framework in which a large language model (LLM)-based agent, equipped with tools and a structured workflow, proposes candidate Hamiltonian and Lindbladian models, generates computational tools, and quantitatively evaluates competing physical hypotheses. We apply the workflow to three previously published experimental cavity-QED datasets collected from color centers in diamond and silicon carbide. Across the experiments, the agent successfully identifies physical models for the system and characterizes the disorder. In assessing reproducibility, we find that run-to-run variability is concentrated primarily in the research plan generation step, while the computational analysis itself is substantially more consistent. Our results suggest that structured LLM agent workflows can support model discovery from quantum optical experimental data while simultaneously allowing for human expert supervision and collaboration.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Quantum Physics (quant-ph) ; Artificial Intelligence (cs.AI); Optics (physics.optics).
Pagina del lavoro: https://arxiv.org/abs/2611.00620 — 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 2026-11-03T10:49:38.498Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).