利用非线性无序介质进行大规模光子计算。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Nature computational science Pub Date : 2024-06-14 DOI:10.1038/s43588-024-00644-1
Hao Wang, Jianqi Hu, Andrea Morandi, Alfonso Nardi, Fei Xia, Xuanchen Li, Romolo Savo, Qiang Liu, Rachel Grange, Sylvain Gigan
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摘要

神经网络广泛应用于科学和技术领域,但由于计算需求不断扩大,在传统计算机中实现神经网络遇到了瓶颈。光子计算是一种前景广阔的神经形态平台,具有大规模并行、超低延迟和降低能耗等潜在优势,但主要用于计算线性运算。在这里,我们展示了一种基于由铌酸锂纳米晶体组成的无序多晶板的大规模、高性能非线性光子神经系统。在随机准相位匹配和多重散射的介导下,线性和非线性光学斑点特征在同时发生的线性随机散射和二次谐波生成的相互作用下产生,定义了一个复杂的神经网络,其中二阶非线性作为内部非线性激活函数。以线性随机投影为基准,这种嵌入了丰富物理计算操作的非线性映射在图像分类、回归和图分类等大量机器学习任务中显示出更高的性能。光学非线性与随机散射的结合可作为可扩展的计算引擎,适用于各种不同的应用,最多可显示 27648 个输入节点和 3500 个非线性输出节点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Large-scale photonic computing with nonlinear disordered media
Neural networks find widespread use in scientific and technological applications, yet their implementations in conventional computers have encountered bottlenecks due to ever-expanding computational needs. Photonic computing is a promising neuromorphic platform with potential advantages of massive parallelism, ultralow latency and reduced energy consumption but mostly for computing linear operations. Here we demonstrate a large-scale, high-performance nonlinear photonic neural system based on a disordered polycrystalline slab composed of lithium niobate nanocrystals. Mediated by random quasi-phase-matching and multiple scattering, linear and nonlinear optical speckle features are generated as the interplay between the simultaneous linear random scattering and the second-harmonic generation, defining a complex neural network in which the second-order nonlinearity acts as internal nonlinear activation functions. Benchmarked against linear random projection, such nonlinear mapping embedded with rich physical computational operations shows improved performance across a large collection of machine learning tasks in image classification, regression and graph classification. Demonstrating up to 27,648 input and 3,500 nonlinear output nodes, the combination of optical nonlinearity and random scattering serves as a scalable computing engine for diverse applications. Nonlinear optical computations have been essential yet challenging for developing optical neural networks with appreciable expressivity. In this paper, light scattering is combined with optical nonlinearity to empower a high-performance, large-scale nonlinear photonic neural system.
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