Setting up experimental Bell test with reinforcement learning
- URL: http://arxiv.org/abs/2005.01697v1
- Date: Mon, 4 May 2020 17:52:10 GMT
- Title: Setting up experimental Bell test with reinforcement learning
- Authors: Alexey A. Melnikov, Pavel Sekatski, Nicolas Sangouard
- Abstract summary: We introduce a method combining reinforcement learning and simulated annealing enabling the automated design of optical experiments.
We illustrate the relevance of our method by applying it to a probability distribution favouring high violations of the Bell-CHSH inequality.
Our method might positively impact the usefulness of photonic experiments for device-independent quantum information processing.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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