  The ability to witness non-local correlations lies at the core of
foundational aspects of quantum mechanics and its application in the processing
of information. Commonly, this is achieved via the violation of Bell
inequalities. Unfortunately, however, their systematic derivation quickly
becomes unfeasible as the scenario of interest grows in complexity. To cope
with that, we propose here a machine learning approach for the detection and
quantification of non-locality. It consists of an ensemble of multilayer
perceptrons blended with genetic algorithms achieving a high performance in a
number of relevant Bell scenarios. Our results offer a novel method and a
proof-of-principle for the relevance of machine learning for understanding
non-locality.
