Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor
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Quantum Physics arXiv:2512.24135 (quant-ph) [Submitted on 30 Dec 2025] Title:Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor Authors:Dario Fasone, Shreyasi Mukherjee, Mauro Paternostro, Elisabetta Paladino, Luigi Giannelli, Giuseppe A. Falci View a PDF of the paper titled Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor, by Dario Fasone and 5 other authors View PDF HTML (experimental) Abstract:We introduce and validate a machine learning-assisted protocol to classify time and space correlations of classical noise acting on a quantum system, using two interacting qubits as probe. We consider different classes of noise, according to their Markovianity and spatial correlations. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various noises are discriminated by only measuring the final transfer efficiencies. This approach reaches around 90% accuracy with a minimal experimental overhead. Comments: Subjects: Quantum Physics (quant-ph); Other Condensed Matter (cond-mat.other) Cite as: arXiv:2512.24135 [quant-ph] (or arXiv:2512.24135v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.24135 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Dario Fasone [view email] [v1] Tue, 30 Dec 2025 10:45:16 UTC (92 KB) Full-text links: Access Paper: View a PDF of the paper titled Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor, by Dario Fasone and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cond-mat cond-mat.other 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?)