# Setting up experimental Bell test with reinforcement learning

arXiv: https://arxiv.org/abs/2005.01697 · HTML: https://mlsanity.com/paper/2005.01697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 1 of 1)

- [Ab-initio experimental violation of Bell inequalities](https://mlsanity.com/paper/2108.00574/) (2021)

## Abstract (arXiv)

Finding optical setups producing measurement results with a targeted probability distribution is hard as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning and simulated annealing enabling the automated design of optical experiments producing results with the desired probability distributions. We illustrate the relevance of our method by applying it to a probability distribution favouring high violations of the Bell-CHSH inequality. As a result, we propose new unintuitive experiments leading to higher Bell-CHSH inequality violations than the best currently known setups. Our method might positively impact the usefulness of photonic experiments for device-independent quantum information processing.