Achieving Sub-Exponential Speedup in Gate-Based Quantum Computing for Quadratic Unconstrained Binary Optimization
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Quantum Physics arXiv:2602.06420 (quant-ph) [Submitted on 6 Feb 2026] Title:Achieving Sub-Exponential Speedup in Gate-Based Quantum Computing for Quadratic Unconstrained Binary Optimization Authors:Ying-Wei Tseng, Yu-Ting Kao, Yeong-Jar Chang, Jia-Han Ou, Wen-Zhi Zhang View a PDF of the paper titled Achieving Sub-Exponential Speedup in Gate-Based Quantum Computing for Quadratic Unconstrained Binary Optimization, by Ying-Wei Tseng and 4 other authors View PDF Abstract:Recent quantum-inspired methods based on the Simulated Annealing algorithm have shown strong potential for solving combinatorial optimization problems. However, Grover's algorithm in gate-based quantum computing offers only a quadratic speedup, which remains impractical for large problem sizes. This paper proposes a hybrid approach that integrates Simulated Annealing with Grover's algorithm to achieve sub-exponential speedup, thereby improving its industrial applicability. In enzyme fermentation, variables such as temperature, stirring, wait time, pH, tryptophan, rice flour and others are encoded by 625 binary parameters, defining the space of possible enzyme formulations. We aim to find a binary configuration that maximizes the active ingredient, formulated as a 625-bit Quadratic Unconstrained Binary Optimization problem generated from historical experiments and artificial intelligence techniques. Minimizing the QUBO cost corresponds to maximizing the active ingredient. This case study demonstrates that the proposed hybrid method achieves sub-exponential speedup using gate-based quantum computing. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.06420 [quant-ph] (or arXiv:2602.06420v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.06420 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yingwei Tseng [view email] [v1] Fri, 6 Feb 2026 06:28:17 UTC (1,112 KB) Full-text links: Access Paper: View a PDF of the paper titled Achieving Sub-Exponential Speedup in Gate-Based Quantum Computing for Quadratic Unconstrained Binary Optimization, by Ying-Wei Tseng and 4 other authorsView PDF view license Current browse context: quant-ph new | recent | 2026-02 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?)