{
  "abstract": "Distributed quantum processors could scale variational algorithms beyond single devices, but circuit depth, communication overhead, and noise limit their performance. We compare ladder, mixed-canonical, and brick-wall realizations of matrix-product-state (MPS) pretraining with matched per-layer two-qubit-block resources. Despite comparable ideal variational quantum eigensolver performance and gradient scales, the shallower brick-wall architecture reduces circuit duration, idle-time decoherence, and zero-noise-extrapolation (ZNE) overhead, yielding superior noisy and ZNE-assisted performance. We then extend MPS-pretrained circuits to modular processors with one communication qubit per quantum processing unit (QPU); a nearest-neighbor QPU-path schedule keeps the per-layer depth constant as QPUs are added. Distributed circuits whose inter-QPU links realize long-range interactions of the target Hamiltonian match or outperform the single-processor brick-wall under noise when communication idle time is short compared with the coherence time. These results establish hardware-aware co-design of tensor-network pretraining, circuit scheduling, and communication topology as a principle for variational quantum computation on noisy modular hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.01099",
  "author_total": 5,
  "authors": [
    {
      "name": "Seongpyo Hong"
    },
    {
      "name": "Woodo Lee"
    },
    {
      "name": "Yong-Su Kim"
    },
    {
      "name": "Seung-Sup B. Lee"
    },
    {
      "name": "Junghyun Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1v7xef45fkdar.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.01099",
  "fulltext_url": null,
  "id": "1v7xef45fkdar",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.01099",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7215989925",
  "pdf_url": "https://arxiv.org/pdf/2610.01099",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1v7xef45fkdar.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.01099",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.01099"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Constant-Per-Layer-Depth MPS-Pretrained Ansatz for Noisy Distributed Quantum Processors",
  "title_text": "Constant-Per-Layer-Depth MPS-Pretrained Ansatz for Noisy Distributed Quantum Processors",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:50Z",
  "url": "https://research.draconitic.com/paper/1v7xef45fkdar",
  "venue": "arXiv",
  "versions": []
}