Setting up experimental Bell test with reinforcement learning

Alexey A Melnikov (1) , Pavel Sekatski (1) , Nicolas Sangouard (1, 2)

1 Unibas - Université de Bâle = University of Basel = Basel Universität
2 IPHT - Institut de Physique Théorique - UMR CNRS 3681

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.

Mots clés: Quantum Physics (quant-ph); Machine Learning (cs.LG)

Domaines: Physique Quantique [quant-ph]

Alexey A Melnikov, Pavel Sekatski, Nicolas Sangouard. Setting up experimental Bell test with reinforcement learning. Physical Review Letters, 2020, 125, pp.160401. ⟨10.1103/PhysRevLett.125.160401⟩. ⟨cea-02955257v2⟩