Setting up experimental Bell test with reinforcement learning

Understand

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.