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Reservoir computing (RC) is a machine learning paradigm that excels at dynamical systems analysis. Photonic RCs, which perform implicit computation through optical interactions, have attracted increasing attention due to their potential for low latency predictions. However, most existing photonic RCs rely on a nonlinear physical cavity to implement system memory, limiting control over the memory structure and requiring long warm-up times to eliminate transients. In this work, we resolve these issues by demonstrating a photonic next-generation reservoir computer (NG-RC) using a fiber optic platform. Our photonic NG-RC eliminates the need for a cavity by generating feature vectors directly from nonlinear combinations of the input data with varying delays. Our approach uses Rayleigh backscattering to produce output feature vectors by an unconventional nonlinearity resulting from coherent, interferometric mixing followed by a quadratic readout. Performing linear optimization on these feature vectors, our photonic NG-RC demonstrates state-of-the-art performance for the observer (cross-prediction) task applied to the R\"ossler, Lorenz, and Kuramoto-Sivashinsky systems. In contrast to digital NG-RC implementations, this scheme is easily scalable to high-dimensional systems while maintaining low latency and low power consumption., Comment: 36 pages, 10 figures

The widespread adoption of machine learning and other matrix intensive computing algorithms has inspired renewed interest in analog optical computing, which has the potential to perform large-scale matrix multiplications with superior energy scaling and lower latency than digital electronics. However, most existing optical techniques rely on spatial multiplexing to encode and process data in parallel, requiring a large number of high-speed modulators and detectors. More importantly, most of these architectures are restricted to performing a single kernel convolution operation per layer. Here, we introduce a fiber-optic computing architecture based on temporal multiplexing and distributed feedback that performs multiple convolutions on the input data in a single layer (i.e. grouped convolutions). Our approach relies on temporally encoding the input data as an optical pulse train and injecting it into an optical fiber where partial reflectors create a series of delayed copies of the input vector. In this work, we used Rayleigh backscattering in standard single mode fiber as the partial reflectors to encode a series of random kernel transforms. We show that this technique effectively performs a random non-linear projection of the input data into a higher dimensional space which can facilitate a variety of computing tasks, including non-linear principal component analysis, support vector machines, or extreme learning machines. By using a passive fiber to perform the kernel transforms, this approach enables efficient energy scaling with orders of magnitude lower power consumption than GPUs, while using a high-speed modulator and detector maintains low latency and high data-throughput. Finally, our approach is readily integrated with fiber-optic communication links, enabling additional applications such as processing remote sensing data transmitted in the analog domain.

Magnetic transition metal chalcogenides form an emerging platform for exploring spin-orbit driven Berry phase phenomena owing to the nontrivial interplay between topology and magnetism. Here we show that the anomalous Hall effect in pristine Cr2Te3 thin films manifests a unique temperature-dependent sign reversal at nonzero magnetization, resulting from the momentum-space Berry curvature as established by first-principles simulations. The sign change is strain tunable, enabled by the sharp and well-defined substrate/film interface in the quasi-two-dimensional Cr2Te3 epitaxial films, revealed by scanning transmission electron microscopy and depth-sensitive polarized neutron reflectometry. This Berry phase effect further introduces hump-shaped Hall peaks in pristine Cr2Te3 near the coercive field during the magnetization switching process, owing to the presence of strain-modulated magnetic domains. The versatile interface tunability of Berry curvature in Cr2Te3 thin films offers new opportunities for topological electronics., Comment: Main: 9 pages, 5 figures; SI: 5 pages, 9 figures