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Setting Up Experimental Bell Tests with Reinforcement Learning.

Alexey A Melnikov1, Pavel Sekatski1, Nicolas Sangouard1,2

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We developed an automated method using AI to design optical experiments for quantum information processing. This approach finds new setups that significantly increase violations of the Bell-CHSH inequality.

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Area of Science:

  • Quantum Information Science
  • Quantum Optics
  • Artificial Intelligence in Physics

Background:

  • Designing optical experiments for specific quantum measurement outcomes is complex due to the exponential growth in possible configurations.
  • Achieving targeted probability distributions is crucial for advancing quantum information processing tasks.

Purpose of the Study:

  • To introduce an automated method for designing optical experiments capable of producing desired probability distributions.
  • To apply this method to optimize for high violations of the Bell-Clauser-Horne-Shimony-Holt (CHSH) inequality.

Main Methods:

  • A novel approach combining reinforcement learning and simulated annealing was employed.
  • The method automates the search for optimal optical experimental setups.

Main Results:

  • The developed method successfully designed optical experiments yielding targeted probability distributions.
  • New, unintuitive experimental setups were discovered that demonstrate higher Bell-CHSH inequality violations than previously known configurations.

Conclusions:

  • The automated design method offers a powerful tool for exploring complex optical experiments.
  • This work has the potential to enhance the utility of photonic experiments in device-independent quantum information processing.