A Quantum Approach to Stochastic Optimization in Insurance Underwriting
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Quantum Physics arXiv:2605.01169 (quant-ph) [Submitted on 2 May 2026] Title:A Quantum Approach to Stochastic Optimization in Insurance Underwriting Authors:Mitchell Bordelon, Maurice Garfinkel, Vivek Dixit, Thomas Whitehead, Jenny Holzbauer, Guillermo Mijares Vilarino, Alberto Maldonado Romo, Abhijit Mitra, Vaibhaw Kumar, Jean Utke View a PDF of the paper titled A Quantum Approach to Stochastic Optimization in Insurance Underwriting, by Mitchell Bordelon and 9 other authors View PDF HTML (experimental) Abstract:The presence of stochastic elements in combinatorial optimization problems makes them particularly challenging, as such problems quickly become intractable for classical computers even at relatively small sizes. In this work, we propose a novel quantum-classical hybrid scheme for solving a class of stochastic optimization problems known as chance-constrained knapsack problems, in which item weights follow probability distributions and constraints may be violated within a specified risk tolerance. Our method employs knapsack-specific QAOA-based circuits to generate samples which, when combined with a self-consistent classical recovery scheme introduced in this work, produce high-quality solutions. Experiments carried out on IBM Heron processors, using circuits with depths up to 177 and comprising 3443 gates acting on as many as 150 qubits, yield solutions that indicate improvement over classical optimization schemes. The proposed quantum-classical scheme paves the way to tackling such problems, with the potential to outperform approaches that rely solely on classical computation. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.01169 [quant-ph] (or arXiv:2605.01169v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.01169 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Vaibhaw Kumar [view email] [v1] Sat, 2 May 2026 00:17:14 UTC (6,963 KB) Full-text links: Access Paper: View a PDF of the paper titled A Quantum Approach to Stochastic Optimization in Insurance Underwriting, by Mitchell Bordelon and 9 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)