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

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arXiv:2603.24665v1 Announce Type: new 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

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Quantum Physics [Submitted on 25 Mar 2026] A versatile neural-network toolbox for testing Bell locality in networks Antoine Girardin, Mohammad Massi Rashidi, Géraldine Haack, Nicolas Brunner, Alejandro Pozas-Kerstjens 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: 10+4 pages, 4+3 figures, RevTeX 4.2. The computational appendix is available at this https URL 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 Submission history From: Alejandro Pozas-Kerstjens [view email] [v1] Wed, 25 Mar 2026 18:00:01 UTC (1,978 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?)

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