Thin perpendicular magnetic anisotropy films between two soft ferromagnetic layers have the nuclei for magnetization inversion at the bifurcations of their characteristic stripe domain pattern. The inverted nuclei induce vortex-antivortex pairs in the soft magnetic layers that exhibit a correlated motion extending several μm along the magnetic stripes during magnetization reversal. The sense of motion is completely determined by the topology of the magnetic bifurcations causing vortex-antivortex pairs to propagate in opposite senses depending on their polarities. This is a robust effect that might have practical applications. These findings are based on X-ray microscopy and micromagnetic calculations.

Hardware-implemented reservoir computing (RC) has been gaining considerable interest in recent years, in particular for classification and nonlinear-prediction tasks. Such RC systems often perform analog computation and, therefore, may be more sensitive to noise than digital systems; noise has been found to often degrade the computational performance. In contrast, here we demonstrate that noise can also play a constructive role in hardware-based RC. Using a hybrid delay-based RC system with an analog part (nonlinearity) and a digital part, we show that the replication of chaotic attractor dynamics is overall improved when the reservoir is trained with an input signal modified by additive Gaussian noise. To quantify the performance of the attractor replication, we suggest two different methods based on recurrence plots and power spectra.

AbstractSemiconductor lasers (SLs) that are subject to delayed optical feedback and external optical injection have been demonstrated to perform information processing using the photonic reservoir computing paradigm. Optical injection or optical feedback can under some conditions induce bandwidth-enhanced operation, expanding their modulation response up to several tens of GHz. However, these conditions may not always result in the best performance for computational tasks, since the dynamical and nonlinear properties of the reservoir might change as well. Here we show that by using strong optical injection we can obtain an increased frequency response and a significant acceleration in the information processing capability of this nonlinear system, without loss of performance. Specifically, we demonstrate numerically that the sampling time of the photonic reservoir can be as small as 12 ps while preserving the same computational performance when compared to a much slower sampling rate. We also show that strong optical injection expands the reservoir’s operating conditions for which we obtain improved task performance. The latter is validated experimentally for larger sampling times of 100 ps. The above attributes are demonstrated in a coherent optical communication decoding task.

No abstract

Time delay reservoir computing (TDRC) using semiconductor lasers (SLs) has proven to be a promising photonic analog approach for information processing. One appealing property is that SLs subject to delayed optical feedback and external optical injection, allow for tuning the response bandwidth by changing the level of optical injection. Here we use strong optical injection, thereby expanding the SL’s modulation response up to tens of gigahertz. Performing a nonlinear time series prediction task, we demonstrate experimentally that for appropriate operating conditions, our TDRC system can operate with sampling times as small as 11.72 ps, without sacrificing computational performance.