# Machine learning non-local correlations

arXiv: https://arxiv.org/abs/1808.07069 · HTML: https://mlsanity.com/paper/1808.07069/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits](https://mlsanity.com/paper/2408.12199/) (2024)
- [Ab-initio experimental violation of Bell inequalities](https://mlsanity.com/paper/2108.00574/) (2021)
- [On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks](https://mlsanity.com/paper/2603.04451/) (2026)
- [The Bell Boundary of Shared Representations](https://mlsanity.com/paper/2610.16828/) (2026)

## Abstract (arXiv)

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.