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arXiv 2501.08079quant-phphysics.comp-ph

量子指导下的网络非本地性学习超越理想资源

Quantum-informed learning of genuine network nonlocality beyond idealized resources

Anantha Krishnan Sunilkumar, Anil Shaji, Debashis Saha

AI总结:

本文提出一种可扩展的因果推断贝叶斯学习框架,用于学习网络贝尔测试中的本地统计,发现新的测量设置具有更强的非本地性,且非本地性仅在共享贝尔态可见性超过0.94时才出现。

摘要:

我们讨论了真正的网络非本地相关性特征,这在非凸本地相关性性质下仍然极具挑战性,即使在三个源和三个观察者实施一次四结果测量的三角情形中。我们引入了一种可扩展的因果推断贝叶斯学习框架,称为分层本地隐藏变量神经网络(分层LHV-Net),用于学习网络贝尔测试中的本地统计。利用该框架,我们识别出一个新的测量设置类别,其非本地性比已知的测量设置更稳健。值得注意的是,我们的研究显示,非本地性度量只有在共享贝尔态的可见性超过0.94时才非零,超过了之前报告的噪声鲁棒性阈值。此外,我们检查了共享状态来自不同来源的相关性,发现只有当所有涉及的状态足够纠缠时,才能观察到非本地性。最后,我们分析了允许源共享经典随机性的场景,发现即使源共享多达3单位的随机性,非本地相关性仍然存在,而本地模型复制量子相关性仅在有4单位共享随机性时才可能实现。除了结果外,这项工作成功展示了量子指导的机器学习方法作为基础框架如何大大促进量子信息领域。

英文摘要:

We address the characterization of genuine network nonlocal correlations, which remain highly challenging due to the non-convex nature of local correlations even in the distinct triangle scenario with three sources and three observers implementing one four-outcome measurement. We introduce a scalable causally inferred Bayesian learning framework called the Layered Local Hidden Variable Neural Network (Layered LHV-Net) to learn the local statistics in network Bell tests. Using this framework, we identify a new class of measurement settings that exhibit the most robust nonlocality compared to previously known measurements. Remarkably, our study shows that the nonlocality measure becomes non-zero only when the visibility of the shared Bell state exceeds 0.94, surpassing previously reported noise robustness thresholds. Further, we examine correlations where shared states originate from dissimilar sources, finding that nonlocality is observed only if all the involved states are sufficiently entangled. Finally, we analyze a scenario in which the sources are allowed to share classical randomness. We find that nonlocal correlations persist even when the sources share up to 3 units of randomness, whereas a local model reproducing the quantum correlations only becomes possible when 4 units of shared randomness are available. Apart from the results, the work succeeds in showing that quantum-informed machine learning approaches as foundational frameworks can greatly benefit the field of quantum information.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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