A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution

Quantum Physics [Submitted on 17 Apr 2026] A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution Asif Akhtab Ronggon, Tasnuva Farheen 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 Submission history From: Asif Akhtab Ronggon [view email] [v1] Fri, 17 Apr 2026 00:59:06 UTC (2,711 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.SE References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)