Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here, we present a neuromorphic photonic scheme, i.e., the photonic extreme learning machine, which can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field acting as a feature mapping space. We experimentally demonstrate learning from data on various classification and regression tasks, achieving accuracies comparable with digital kernel machines and deep photonic networks. Our findings point out an optical machine learning device that is easy to train, energetically efficient, scalable, and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.

中文翻译：

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基于自由空间光传播的光子极限学习机

受光子大脑启发的平台正在成为新型模拟计算设备，为机器学习提供快速和节能的操作。这些人工神经网络通常需要定制的光学元件，例如集成光子电路、工程衍射层、纳米光子材料或延时方案，这些都很难训练或稳定。在这里，我们提出了一种神经形态光子方案，即光子极限学习机，它可以简单地通过使用光学编码器和自由空间中的相干波传播来实现。我们通过激光束的空间光调制来实现这一概念，远场充当特征映射空间。我们通过实验证明了从各种分类和回归任务的数据中学习，实现与数字内核机器和深度光子网络相当的精度。我们的发现指出了一种易于训练、能量高效、可扩展且不受制造限制的光学机器学习设备。该方案可以推广到过多的光子系统，为光学数据的实时神经形态处理开辟了道路。