{
  "abstract": "Loan portfolio optimization (LPO) seeks portfolios that minimize credit risk while satisfying practical selection constraints. Accounting for portfolio-level risk requires modeling not only expected losses but also loss variability and borrower-default correlations, leading to a large-scale combinatorial optimization problem whose complexity grows rapidly with portfolio size. In this work, we formulate LPO as a Quadratic Constrained Binary Optimization (QCBO) problem that jointly captures expected and unexpected losses and transform the resulting formulation into a Quadratic Unconstrained Binary Optimization (QUBO) model suitable for variational quantum optimization. To address the limited qubit resources of current quantum hardware, we employ Pauli Correlation Encoding (PCE), which enables a large number of portfolio-selection variables to be represented using comparatively few qubits. The proposed framework is evaluated on a real-world microfinance dataset comprising 2012 borrowers. For problem instances with up to 1000 portfolio-selection variables, we benchmark classical simulations of a variational quantum algorithm (VQA) against Google OR-Tools under matched time budgets, obtaining feasible portfolios with an objective-value gap of about 40% across all sizes. We further implement the framework on superconducting quantum hardware for instances containing up to 1500 variables. Across these instances, the experimentally obtained objective values are within 10% of the corresponding simulation results. These findings demonstrate the feasibility of applying PCE-based variational quantum optimization to large-scale loan portfolio optimization under current quantum hardware limitations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30195",
  "author_total": 7,
  "authors": [
    {
      "name": "Balaganchi A. Bhargava"
    },
    {
      "name": "Kamlesh Kumar"
    },
    {
      "name": "Gouranga Dinda"
    },
    {
      "name": "Aniruddha Biswas"
    },
    {
      "name": "Vijay S. Rao"
    },
    {
      "name": "Debarag Banerjee"
    },
    {
      "name": "Arun Sehrawat"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/drx4htjpfxc4v.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30195",
  "fulltext_url": null,
  "id": "drx4htjpfxc4v",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30195",
  "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": "W7214298607",
  "pdf_url": "https://arxiv.org/pdf/2609.30195",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/drx4htjpfxc4v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2609.30195",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30195"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Loan Portfolio Optimization with Variational Quantum Algorithms",
  "title_text": "Loan Portfolio Optimization with Variational Quantum Algorithms",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/drx4htjpfxc4v",
  "venue": "arXiv",
  "versions": []
}