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A versatile neural-network toolbox for testing Bell locality in networks

arXiv Quantum Physics
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⚡ Quantum Brief
Researchers from Switzerland and Spain developed a neural-network-based toolbox to test Bell locality in quantum networks, addressing a long-standing challenge in quantum foundations. The software leverages machine learning optimization to determine whether observed quantum correlations can be explained by local hidden variables. The toolbox builds on a 2019 framework but introduces significant performance improvements, making it applicable to arbitrary network configurations. Its user-friendly design democratizes access to complex nonlocality testing for experimental and theoretical physicists. The team applied the toolbox to unexplored quantum networks, uncovering new insights into quantum nonlocal sets. These findings suggest practical pathways for realizing quantum nonlocal correlations in real-world experiments. Technical advancements include optimized neural network parameterization of local models, enabling faster convergence and more reliable detection of nonlocality. This bridges quantum information theory with modern machine learning techniques. The work provides both a theoretical framework and practical software solution, advancing the study of network nonlocality. It offers concrete tools for testing quantum advantage in distributed quantum systems.
A versatile neural-network toolbox for testing Bell locality in networks

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Quantum Physics arXiv:2603.24665 (quant-ph) [Submitted on 25 Mar 2026] Title:A versatile neural-network toolbox for testing Bell locality in networks Authors:Antoine Girardin, Mohammad Massi Rashidi, Géraldine Haack, Nicolas Brunner, Alejandro Pozas-Kerstjens View a PDF of the paper titled A versatile neural-network toolbox for testing Bell locality in networks, by Antoine Girardin and Mohammad Massi Rashidi and G\'eraldine Haack and Nicolas Brunner and Alejandro Pozas-Kerstjens View PDF HTML (experimental) Abstract:Determining whether an observed distribution of events generated in a quantum network is Bell local, i.e., if it admits an alternative realization in terms of independent local variables, is extremely challenging. Building upon arXiv:1907.10552, we develop a software solution that parameterizes local models in networks via neural networks. This allows one to leverage optimization tools available from the machine learning community in the search of network Bell nonlocality. Our solution applies to arbitrary networks, is easy to use, and includes technical improvements that significantly increase performance compared to previous implementations. We apply it to investigate nonlocality in several networks hitherto unexplored, providing insights on the corresponding quantum nonlocal sets and suggesting concrete, promising realizations of quantum nonlocal correlations. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.24665 [quant-ph] (or arXiv:2603.24665v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.24665 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Alejandro Pozas-Kerstjens [view email] [v1] Wed, 25 Mar 2026 18:00:01 UTC (1,978 KB) Full-text links: Access Paper: View a PDF of the paper titled A versatile neural-network toolbox for testing Bell locality in networks, by Antoine Girardin and Mohammad Massi Rashidi and G\'eraldine Haack and Nicolas Brunner and Alejandro Pozas-KerstjensView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-03 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?)

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Source: arXiv Quantum Physics