Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers
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Quantum Physics arXiv:2604.02416 (quant-ph) [Submitted on 2 Apr 2026] Title:Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers Authors:Edoardo Alessandroni, Sergi Ramos-Calderer, Michel Krispin, Fritz Schinkel, Stefan Walter, Martin Kliesch, Leandro Aolita, Ingo Roth View a PDF of the paper titled Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers, by Edoardo Alessandroni and 7 other authors View PDF HTML (experimental) Abstract:Quadratic unconstrained binary optimization (QUBO) provides problem formulations for various computational problems that can be solved with dedicated QUBO solvers, which can be based on classical or quantum computation. A common approach to constrained combinatorial optimization problems is to enforce the constraints in the QUBO formulation by adding penalization terms. Penalization introduces an additional hyperparameter that significantly affects the solver's efficacy: the relative weight between the objective terms and the penalization terms. We develop a pre-computation strategy for determining penalization weights with provable guarantees for Gibbs solvers and polynomial complexity for broad problem classes. Experiments across diverse problems and solver architectures, including large-scale instances on Fujitsu's Digital Annealer, show robust performance and order-of-magnitude speedups over existing heuristics. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.02416 [quant-ph] (or arXiv:2604.02416v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.02416 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Edoardo Alessandroni [view email] [v1] Thu, 2 Apr 2026 18:00:02 UTC (2,130 KB) Full-text links: Access Paper: View a PDF of the paper titled Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers, by Edoardo Alessandroni and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-04 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?)