{
  "abstract": "The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-network agent adaptively selects the two-body operators at each iteration, yielding more compact ansätze and improved robustness with respect to critical hyperparameters. A key feature of the algorithm is a scalable state representation based on the ACSE residuals, whose dimension grows with the one-particle basis but remains independent of the number of targeted excited states. We also verify the equivalence of sign-free qubit operators in the excited-state setting, extending a result previously established for ground-state problems. Our RL-CQE for time evolution derives from a constant-scaling ansatz that represents the wave function with a fixed number of unitary transformations independent of simulation time $t$, enabled by the shared unitary structure of the purified ensemble treatment of excited states. Benchmarks on chemical systems demonstrate chemical accuracy with minimal operator counts across a range of bond lengths.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.18569",
  "author_total": 3,
  "authors": [
    {
      "name": "Jiaji Zhang"
    },
    {
      "name": "Lipeng Chen"
    },
    {
      "name": "Carlos L. Benavides-Riveros"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0nfezmn53ze4y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.18569",
  "fulltext_url": null,
  "id": "0nfezmn53ze4y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.18569",
  "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",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.18569",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0nfezmn53ze4y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2605.18569",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.18569"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics",
  "title_text": "Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/0nfezmn53ze4y",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1021/acs.jctc.6c00988",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}