Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditional wave materials, such analog processors have been so far largely confined to simple linear projections such as image edge detection or matrix multiplications. Complex neuromorphic computing tasks, which inherently require strong non-linearities, have so far remained out-of-reach of wave-based solutions, with a few attempts that implemented non-linearities on the digital front, or used weak and inflexible non-linear sensors, restraining the learning performance. Here, we tackle this issue by demonstrating the relevance of Time-Floquet physics to induce a strong non-linear entanglement between signal inputs at different frequencies, enabling a power-efficient and versatile wave platform for analog extreme deep learning involving a single, uniformly modulated dielectric layer and a scattering medium. We prove the efficiency of the method for extreme learning machines and reservoir computing to solve a range of challenging learning tasks, from forecasting chaotic time series to the simultaneous classification of distinct datasets. Our results open the way for wave-based machine learning with high energy efficiency, speed, and scalability.

中文翻译：

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基于非线性时间-Floquet纠缠的基于波的深度学习

基于波的模拟信号处理有望实现极快、动态、节能的数据处理，当波通过人工工程介质传播时发生。然而，由于传统波材料从根本上来说非线性很弱，这种模拟处理器迄今为止主要局限于简单的线性投影，例如图像边缘检测或矩阵乘法。复杂的神经形态计算任务本质上需要很强的非线性，到目前为止，基于波的解决方案仍然遥不可及，有一些尝试在数字前端实现非线性，或者使用弱和不灵活的非线性传感器，抑制学习性能。这里，我们通过展示 Time-Floquet 物理学在不同频率的信号输入之间引起强烈非线性纠缠的相关性来解决这个问题，从而为模拟极端深度学习提供一个节能且多功能的波平台，涉及单个均匀调制的介电层和散射介质。我们证明了极限学习机和储层计算方法的效率，可以解决一系列具有挑战性的学习任务，从预测混沌时间序列到不同数据集的同时分类。我们的结果为具有高能效、速度和可扩展性的基于波的机器学习开辟了道路。均匀调制的介电层和散射介质。我们证明了极限学习机和储层计算方法的效率，可以解决一系列具有挑战性的学习任务，从预测混沌时间序列到不同数据集的同时分类。我们的结果为具有高能效、速度和可扩展性的基于波的机器学习开辟了道路。均匀调制的介电层和散射介质。我们证明了极限学习机和储层计算方法的效率，可以解决一系列具有挑战性的学习任务，从预测混沌时间序列到不同数据集的同时分类。我们的结果为具有高能效、速度和可扩展性的基于波的机器学习开辟了道路。