Setting Up Experimental Bell Tests with Reinforcement Learning

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 re...

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Bibliographic Details
Published in:Physical review letters 2020-10, Vol.125 (16), p.160401-160401, Article 160401
Main Authors: Melnikov, Alexey A., Sekatski, Pavel, Sangouard, Nicolas
Format: Article
Language:English
Subjects:
Data processing
Experiments
Learning
Probability distribution
Quantum phenomena
Simulated annealing
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Summary: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-Clauser-Horne-Shimony-Holt (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.
ISSN:0031-9007
1079-7114
DOI:10.1103/PhysRevLett.125.160401