论文标题
通过增强学习设置实验性铃铛测试
Setting up experimental Bell test with reinforcement learning
论文作者
论文摘要
找到具有目标概率分布的测量结果的光学设置很困难，因为可能的实验实现数量随着模式和设备数量而成倍增长。为了应对这种复杂性，我们引入了一种结合加固学习和模拟退火的方法，从而使光学实验的自动设计具有所需的概率分布。我们通过将其应用于有利于对贝尔-CHSH不平等的高度侵犯的概率分布来说明我们的方法的相关性。结果，我们提出了与当前最著名的设置相比，新的不直觉实验导致违反贝尔·奇什的不平等现象。我们的方法可能会积极影响光子实验对设备无关的量子信息处理的有用性。
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.
