{
  "id": 13690,
  "url": "https://arxiv.org/abs/2607.22468v1",
  "title": "Learning to Prepare Molecular Ground States with Transformer Models",
  "summary": "Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistry applications. Iterative algorithms like ADAPT-VQE can produce shallow ground-state preparation circuits, but become computationally prohibitive for the larger molecules relevant to materials science and pharmaceutical development. Here, we introduce ADAPT-GQE, a generative AI framework that learns to synthesize groun",
  "authors": "Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit, Marwa H. Farag, Kripa Panchagnula, Gabriel Laude, Fabian Finger, Carlo Gaggioli, Ludmila Szulakowska, Oliver J. Backhouse, Christos Papalitsas, Jason G. Mustakis, Thomas Soini, David Munoz Ramo, Stephen Clark, Elica Kyoseva, Enrico Rinaldi",
  "category": "research",
  "topics": "biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T16:32:40.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13690",
  "original_url": "https://arxiv.org/abs/2607.22468v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}