Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.
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大气效应（例如湍流和背景热噪声）抑制了在开关键控自由空间光学通信中使用的相干光的传播。在这里，我们介绍并实验验证了卷积神经网络，以降低后处理中自由空间光学通信的位错误率，而自由空间光学通信的位比基于高级光学器件的现有解决方案明显简单，更便宜。我们的方法由两个神经网络组成，这是第一个确定在热噪声和湍流中存在相干位序列以及第二个解调相干位序列的存在。通过生成连贯的光线，将它们与热灯结合在一起，并通过湍流的水箱将其结合起来，通过生成开关的键入键流，可以通过实验获得我们网络的所有数据，从而获得了模拟的湍流，并将其传递给了最终的光线。高度准确性。我们的卷积神经网络提高了与阈值分类方案相比的检测准确性，并具有与当前解调和误差校正方案集成的能力。
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