Resource-Efficient Quantum Optimization via Higher-Order Encoding

Quantum Physics arXiv:2511.17545 (quant-ph) [Submitted on 10 Nov 2025] Title:Resource-Efficient Quantum Optimization via Higher-Order Encoding Authors:Frederik Koch, Shahram Panahiyan, Rick Mukherjee, Joseph Doetsch, Dieter Jaksch View a PDF of the paper titled Resource-Efficient Quantum Optimization via Higher-Order Encoding, by Frederik Koch and 4 other authors View PDF Abstract:Quantum approaches to combinatorial optimization problems (COPs) are often limited by the resource demands of Quadratic Unconstrained Binary Optimization (QUBO) encodings, which enlarge circuits through penalty terms and increase qubit and gate counts. We show that Higher-Order Unconstrained Binary Optimization (HUBO) enables a more resource-efficient formulation. Our method systematically constructs HUBO Hamiltonians and, compared to QUBO in benchmarks on Gate Assignment (GAP), Maximum k-Colorable Subgraph (MkCS), and Integer Programming (IP) problems, exponentially reduces qubit requirements and decreases CNOT gate counts by at least 89.6% after compilation to single- and two-qubit gates for all tested instances. These results highlight HUBO as a practical alternative for current and near-term devices. To promote adoption, we release an open-source Python library that automates HUBO model construction, broadening access to resource-efficient quantum optimization. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2511.17545 [quant-ph] (or arXiv:2511.17545v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2511.17545 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Frederik Koch [view email] [v1] Mon, 10 Nov 2025 10:17:55 UTC (884 KB) Full-text links: Access Paper: View a PDF of the paper titled Resource-Efficient Quantum Optimization via Higher-Order Encoding, by Frederik Koch and 4 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2025-11 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)