{
  "abstract": "We show that a class of binary optimization problems with complex non-quadratic objectives or constraints can be reformulated as multi-objective quadratic unconstrained binary optimization problems. When the objective and constraints depend on a small number of quadratic features and are monotone with respect to their preferred directions, at least one globally optimal solution lies in the Pareto set of the associated MO-QUBO. This enables the constraints to be evaluated classically on Pareto-optimal candidates rather than encoded as penalties. We demonstrate the approach for binary portfolio optimization under a Conditional Value-at-Risk constraint. Using Quantum Approximate Multi-Objective Optimization on an illustrative 100-asset instance, we approximate the mean-variance Pareto front using an IBM Quantum computer and derive mean-CVaR fronts through classical post-processing. The hardware results recover the overall structure of the classical front and yield near-optimal feasible portfolios for different risk bounds.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13790",
  "author_total": 3,
  "authors": [
    {
      "name": "Andres Ruiz"
    },
    {
      "name": "Soumyadip Ghosh"
    },
    {
      "name": "Stefan Woerner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ccean025rbs1r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13790",
  "fulltext_url": null,
  "id": "ccean025rbs1r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13790",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7213253725",
  "pdf_url": "https://arxiv.org/pdf/2609.13790",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ccean025rbs1r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2609.13790",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13790"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Binary Optimization with Complex Constraints via Quantum Approximate Multi-Objective Optimization",
  "title_text": "Binary Optimization with Complex Constraints via Quantum Approximate Multi-Objective Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/ccean025rbs1r",
  "venue": "arXiv",
  "versions": []
}