{
  "abstract": "Designing quantum optical experiments requires searching over discrete circuit topologies and continuous parameters, often with multiple realizations of the same target state. Graph-based methods commonly address this problem by optimizing a dense graph and pruning it toward a single circuit. We introduce \\texttt{Grinch}, a bottom-up reward-driven generative framework that learns to sample optical graphs directly from fidelity-based rewards without relying on a pre-existing training dataset. We demonstrate the framework on multipartite entangled states, graph and cluster states, and nonlocal photonic-gate targets, obtaining multiple high-fidelity optical graphs. We identify asymptotic solutions for states that cannot be generated exactly by graphs, as well as nonlocal Toffoli gate constructions requiring two ancillas. Our work presents hardware-constraint objectives, which lead to alternative constructions of CNOT and Toffoli gates when connections between ancilla nodes are restricted. These findings illustrate a reward-driven approach to quantum optical inverse design, exploring alternative circuit topologies while directly incorporating connectivity constraints into the search process.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08073",
  "author_total": 3,
  "authors": [
    {
      "name": "Isaac L. Huidobro-Meezs"
    },
    {
      "name": "Simón Paiva-Ortega"
    },
    {
      "name": "Rodrigo A. Vargas-Hernández"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/agxke7tef9y4r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08073",
  "fulltext_url": null,
  "id": "agxke7tef9y4r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08073",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7212075638",
  "pdf_url": "https://arxiv.org/pdf/2609.08073",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/agxke7tef9y4r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:49Z",
      "native_id": "2609.08073",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08073"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Bottom-Up Design of Quantum Optical Experiments Using Discrete Generative Models",
  "title_text": "Bottom-Up Design of Quantum Optical Experiments Using Discrete Generative Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:49Z",
  "url": "https://research.draconitic.com/paper/agxke7tef9y4r",
  "venue": "arXiv",
  "versions": []
}