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.

中文翻译：

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通过强化学习设置实验性贝尔测试

先验地找到光学装置以产生具有目标概率分布的测量结果是困难的可能的实验实现方式的数量随着模式数量和设备数量的增加而呈指数增长。为了解决这种复杂性，我们引入了一种结合强化学习和模拟退火的方法，从而能够自动设计光学实验，从而产生具有所需概率分布的结果。我们通过将其应用于有利于高度违反Bell-Clauser-Horne-Shimony-Holt（CHSH）不等式的概率分布来说明我们方法的相关性。结果，我们提出了新的，不直观的实验，导致比目前已知的最佳设置更高的违反Bell-CHSH不等式的行为。我们的方法可能会积极影响光子实验对与设备无关的量子信息处理的有效性。