A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution
Summarize this article with:
Quantum Physics arXiv:2604.15603 (quant-ph) [Submitted on 17 Apr 2026] Title:A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution Authors:Asif Akhtab Ronggon, Tasnuva Farheen View a PDF of the paper titled A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution, by Asif Akhtab Ronggon and 1 other authors View PDF HTML (experimental) Abstract:Current fault-tolerant quantum compilers allocate error budgets uniformly during resource estimation, causing suboptimal physical resource overhead. We optimize this allocation using a potential game formulation, where Nash Equilibrium yields a Pareto-optimal distribution across logical operations, T-state distillation, and rotation synthesis. An iterated best response (IBR) algorithm converges to this equilibrium through monotonic descent of the shared cost function. Evaluation across 433 MQT benchmarks demonstrates an average reduction of 30.22\% in physical resource requirements relative to uniform baselines, with peak improvements of 97.81\% for specific circuit instances. This establishes a game-theoretic foundation for strategic error budget optimization in fault-tolerant quantum design automation. Subjects: Quantum Physics (quant-ph); Software Engineering (cs.SE) Cite as: arXiv:2604.15603 [quant-ph] (or arXiv:2604.15603v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.15603 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Asif Akhtab Ronggon [view email] [v1] Fri, 17 Apr 2026 00:59:06 UTC (2,711 KB) Full-text links: Access Paper: View a PDF of the paper titled A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution, by Asif Akhtab Ronggon and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-04 Change to browse by: cs cs.SE 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?)