Machine learning non-local correlations

TL;DR

This paper introduces a machine learning approach using neural networks and genetic algorithms to detect and quantify non-local correlations in quantum mechanics, addressing the complexity of Bell inequality scenarios.

Contribution

It presents a novel machine learning method that efficiently identifies non-locality in complex quantum scenarios, surpassing traditional analytical techniques.

Findings

High performance in detecting non-local correlations

Effective in complex Bell scenarios

Demonstrates machine learning's relevance in quantum foundations

Abstract

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.