{
  "abstract": "While parameterized quantum computations have shown success in standard reinforcement learning (RL), whether these advantages adapt to hierarchical RL (HRL) remains a critical open question. This work demonstrates that variational quantum circuits (VQCs) can effectively enhance HRL agents based on the option-critic architecture. Evaluated in standard environments, a hybrid HRL agent with a quantum feature extractor outperforms classical baselines while using fewer parameters. We also identify an architectural bottleneck: using VQCs for option-value estimation severely degrades learning. Further ablations reveal how quantum circuit design affects performance. Our work establishes design principles for parameter-efficient hybrid HRL agents.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.03434",
  "author_total": 2,
  "authors": [
    {
      "name": "Yu-Ting Lee"
    },
    {
      "name": "Samuel Yen-Chi Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8b9sv3tx0qb2z.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.03434",
  "fulltext_url": null,
  "id": "8b9sv3tx0qb2z",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.03434",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7160421993",
  "pdf_url": "https://arxiv.org/pdf/2605.03434",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8b9sv3tx0qb2z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2605.03434",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.03434"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits",
  "title_text": "Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/8b9sv3tx0qb2z",
  "venue": "arXiv",
  "versions": []
}