Physics-Informed Learning of Effective Error Processes from Limited Noisy Transmon Measurements for Robust QAOA Reliability
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Quantum Physics arXiv:2606.00353 (quant-ph) [Submitted on 29 May 2026] Title:Physics-Informed Learning of Effective Error Processes from Limited Noisy Transmon Measurements for Robust QAOA Reliability Authors:Ebrahim Khaleghian, Özgür E. Müstecaplıoğlu View a PDF of the paper titled Physics-Informed Learning of Effective Error Processes from Limited Noisy Transmon Measurements for Robust QAOA Reliability, by Ebrahim Khaleghian and 1 other authors View PDF HTML (experimental) Abstract:We present a physics-informed pipeline for learning effective quantum error processes from finite-shot measurements generated by hidden transmon-like simulators. Each physical transmon is modeled as an imperfect qutrit, while the learner only receives limited tomography data rather than microscopic Hamiltonian parameters. The learned representations are compact effective models: local affine Bloch channels for each qubit and, in the three-qubit extension, pairwise residuals that capture correlated errors. The learned error models are evaluated operationally by their ability to mitigate the cost landscape of the Quantum Approximate Optimization Algorithm (QAOA) for MaxCut. A two-qubit proof of concept shows that a neural-network approach can infer a full 24-parameter effective channel from only 12 local tomography values and improve QAOA landscape reliability by about $20.4\times$. A scaled three-qubit study shows that local structured learning still strongly improves QAOA reliability: at $K=18$ local measurements, Ridge regression and the neural-network approach reduce QAOA mean absolute error from about $0.1775$ to $0.0269$ and $0.0306$, respectively. Pair probes substantially improve correlated-error identifiability, reducing pair-residual L2 error from about $1.731$ to $1.122$. These results support effective error-process learning as a hardware-aware route toward more reliable variational quantum algorithms. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.00353 [quant-ph] (or arXiv:2606.00353v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.00353 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ebrahim Khaleghian [view email] [v1] Fri, 29 May 2026 20:58:00 UTC (2,910 KB) Full-text links: Access Paper: View a PDF of the paper titled Physics-Informed Learning of Effective Error Processes from Limited Noisy Transmon Measurements for Robust QAOA Reliability, by Ebrahim Khaleghian and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-06 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?)