Universal method for optimized robustness in self-testing of quantum resources

Quantum Physics [Submitted on 20 Mar 2026] Universal method for optimized robustness in self-testing of quantum resources Shin-Liang Chen, Nikolai Miklin Self-testing is a phenomenon where the use of specific quantum states or measurements can be inferred solely from the correlations they generate. We introduce a universal method for conducting robustness analysis in the self-testing of various quantum resources. Unlike previous numerical approaches, which rely on selecting specific isometries, our method optimizes over equivalence transformations, thereby leading to tighter robustness bounds. This optimization employs the well-established technique of semidefinite programming relaxations for non-commuting polynomial optimization. Our method can be universally applied to diverse self-testing settings, including steerable assemblages in the Bell scenario, constellations of quantum states in the prepare-and-measure scenario, and entangled states in the steering scenario. We demonstrate the method's capability to surpass previously reported robustness bounds across a range of concrete examples. Comments: 10 pages, 4 figures, comments welcome! Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.19612 [quant-ph]   (or arXiv:2603.19612v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.19612 Focus to learn more Submission history From: Shin-Liang Chen [view email] [v1] Fri, 20 Mar 2026 03:37:20 UTC (442 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 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?)