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This research introduces a neural-network-based toolbox designed to efficiently test Bell locality within complex quantum networks, bridging machine learning optimization with fundamental quantum physics. By parameterizing local models via artificial intelligence, the approach significantly outperforms previous implementations in identifying nonlocal correlations across arbitrary network topologies. The work provides actionable insights for realizing promising quantum nonlocality protocols that were previously difficult to explore computationally.
A versatile neural-network toolbox for testing Bell locality in networks
arXiv:2603.24665v2 Announce Type: replace 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.
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.
11+4 pages, 4+3 figures, 1 table, RevTeX 4.2. The computational appendix is available at this https URL V2: Added section V.B with new results, close to published version
arXiv:2603.24665 [quant-ph]
(or arXiv:2603.24665v2 [quant-ph] for this version)
Quantum Sci. Technol. 11 035056 (2026)
https://doi.org/10.1088/2058-9565/ae8df7 DOI(s) linking to related resources
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