{
  "abstract": "Quantum computing offers promising approaches to combinatorial optimization problems whose search spaces grow exponentially with problem size. The Closest String problem belongs to this category, which seeks a string that minimizes the maximum Hamming distance to any string in a given collection of reference strings. In this work, we present an end-to-end quantum implementation of the Closest String problem using the Non-Variational Quantum Walk Optimization Algorithm (NV-QWOA), evaluating its resource requirements and solution amplification capabilities. Our automated workflow converts arbitrary problem instances into quantum circuits expressed in one- or two-qubit gates that encode candidate solutions, evaluate the objective function, and apply NV-QWOA optimisation to increase the measurement probabilities of high-quality solutions. Analytical resource estimates yield a qubit complexity of $\\mathcal{O}(nm\\log k)$ and a circuit depth of $\\mathcal{O}(pm\\log^2 n + pn\\log n)$, where $n$ is the string length, $m$ is the number of reference strings, $k$ is the alphabet size, and $p$ is the number of NV-QWOA iterations. We introduce a revised prescription for the phase-separation parameter, $γ$, that depends on problem size but requires no instance-specific optimization. This prescription mitigates excessive phase wrapping modulo $2π$, which can weaken the relationship between objective values and the phases guiding the optimization dynamics. Across all simulated problem sizes, the algorithm consistently amplifies optimal and near-optimal candidates. This work provides an explicit gate-level implementation, an improved phase-scaling method, and a foundation for hardware experiments and benchmarking against classical algorithms.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.33532",
  "author_total": 2,
  "authors": [
    {
      "name": "Benjamin Rossdeutscher"
    },
    {
      "name": "Jingbo Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/czf0r3gwr92ex.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33532",
  "fulltext_url": null,
  "id": "czf0r3gwr92ex",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33532",
  "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": "W7223839265",
  "pdf_url": "https://arxiv.org/pdf/2610.33532",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/czf0r3gwr92ex.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.33532",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33532"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Designing Quantum Solutions to the Closest String Problem with the Non-Variational QWOA",
  "title_text": "Designing Quantum Solutions to the Closest String Problem with the Non-Variational QWOA",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/czf0r3gwr92ex",
  "venue": "arXiv",
  "versions": []
}