In the field of disordered photonics, a common objective is to characterize optically opaque materials for controlling light delivery or performing imaging. Among various complex devices, multi-mode optical fibers stand out as cost-effective and easy-to-handle tools, making them attractive for several tasks. In this context, we leverage the reservoir computing paradigm to recast these fibers into random hardware projectors, transforming an input dataset into a higher dimensional speckled image set. The goal of our study is to demonstrate that using such randomized data for classification by training a single logistic regression layer improves accuracy compared to training on direct raw images. Interestingly, we found that the classification accuracy achieved using the reservoir is also higher than that obtained with the standard transmission matrix model, a widely accepted tool for describing light transmission through disordered devices. We find that the reason for such improved performance could be due to the fact that the hardware classifier operates in a flatter region of the loss landscape when trained on fiber data, which aligns with the current theory of deep neural networks. These findings strongly suggest that multi-mode fibers possess robust generalization properties, positioning them as promising tools for optically-assisted neural networks. With this study, in fact, we want to contribute to advancing the knowledge and practical utilization of these versatile instruments, which may play a significant role in shaping the future of machine learning.

翻译：在无序光子学领域，一个常见的目标是表征光学不透明材料，以控制光传输或实现成像。在各种复杂器件中，多模光纤因其成本低廉且易于操作而脱颖而出，成为多项任务中的吸引性工具。在此背景下，我们利用储备池计算范式将这些光纤重构为随机硬件投影器，将输入数据集转换为更高维度的散斑图像集。本研究旨在证明，通过训练单一逻辑回归层来使用此类随机化数据进行分类，其准确性优于直接训练原始图像。有趣的是，我们发现使用储备池实现的分类准确率也高于标准传输矩阵模型（一种广泛接受用于描述无序器件光传输的工具）。我们推测，这种性能提升的原因在于硬件分类器在处理光纤数据时，其损失函数作用于更平坦的区域，这与当前深度神经网络理论相一致。这些发现强烈表明，多模光纤具有鲁棒的泛化特性，使其成为光学辅助神经网络的理想工具。实际上，通过本研究，我们希望推动对这些多功能仪器知识认知与实际应用的进步，它们可能在未来机器学习发展中发挥重要作用。