Setting up experimental Bell test with reinforcement learning
This work addresses the complexity of experimental design in photonic quantum information processing, offering an automated approach that could enhance device-independent applications.
The authors tackled the challenge of designing optical experiments with targeted probability distributions by introducing a reinforcement learning and simulated annealing method, resulting in new setups that achieve higher Bell-CHSH inequality violations than previous best-known ones.
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.