Experimental Bell Tests with Reinforcement Learning

a) Schematic representation of a Bell test with two parties—Alice and Bob. In each round, Alice and Bob independently perform a measurement on the state that they share. The measurement outcomes that Alice and Bob observe by repeating the measurements are used to test a Bell inequality. (b) Schematic representation of the proposed learning protocol to design photonic experiments leading to a probability distribution of measurement outcomes favoring a large CHSH inequality violation. Reinforcement learning (gray-green arrows) and simulated annealing (blue arrows) approaches are used together.

Terra Quantum, a Swiss startup, and University of Basel have developed a combination of reinforcement learning and simulated annealing for setting up efficient Bell tests useful for quantum cryptography. 

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, they introduced a method combining reinforcement learning and simulated annealing enabling the automated design of optical experiments producing results with the desired probability distributions.

They illustrated the relevance of our method by applying it to a probability distribution favouring high violations of the Bell-Clauser-Horne-Shimony-Holt (CHSH) inequality.

As a result, they have proposed new unintuitive experiments leading to higher Bell-CHSH inequality violations than the best currently known setups.

Their method might positively impact the usefulness of photonic experiments for device-independent quantum information processing.

The paper has been published in Physical Review Letters.