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

TL;DR

This paper introduces a flexible neural network-based software toolbox for testing Bell locality in quantum networks, enabling efficient exploration of nonlocal correlations in complex network configurations.

Contribution

It develops a versatile neural network framework that improves performance and applicability for assessing Bell nonlocality in arbitrary quantum networks.

Findings

Successfully applied to new network configurations

Revealed insights into quantum nonlocal sets

Suggested promising quantum nonlocal correlation realizations

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.