Machine learning non-local correlations
This provides a novel method for researchers in quantum information processing to handle complex non-locality scenarios, though it appears incremental as it applies existing ML techniques to a known bottleneck.
The authors tackled the problem of detecting and quantifying non-local correlations in quantum mechanics, which becomes unfeasible with traditional Bell inequalities as scenarios grow complex, by proposing a machine learning approach using an ensemble of multilayer perceptrons blended with genetic algorithms that achieves high performance in relevant Bell scenarios.
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.