This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained supervised learning algorithms that can predict the degree of entanglement in the two-mode state as well as perform the full tomography of one photonic mode, obtaining satisfactory values in the considered regression metrics.

中文翻译：

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光子电路中的量子模式识别

本文提出了一种机器学习方法，通过简单的光路和光子数分布的数据处理来表征光子状态，例如光子模式。输入状态由用作参考的两个相干状态和要研究的双模式未知状态组成。我们成功训练了监督学习算法，该算法可以预测双模式状态下的纠缠程度，并执行一种光子模式的完整断层扫描，在考虑的回归指标中获得令人满意的值。