Post-processing methods for delay embedding and feature scaling of reservoir computers.

Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts.

Efficient optimisation of physical reservoir computers using only a delayed input.

Reservoir computing is a machine learning algorithm for processing time dependent data which is well suited for experimental implementation. Tuning the hyperparameters of the reservoir is a time-consuming task that limits is applicability. Here we present an experimental validation of a recently proposed optimisation technique in which the reservoir receives both the input signal and a delayed version of the input signal.

Data-informed reservoir computing for efficient time-series prediction.

We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available.

Multiplexed random-access optical memory in warm cesium vapor.

The ability to store large amounts of photonic quantum states is regarded as substantial for future optical quantum computation and communication technologies. However, research for multiplexed quantum memories has been focused on systems that show good performance only after an elaborate preparation of the storage media. This makes it generally more difficult to apply outside a laboratory environment.

Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics.

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation.

Deriving task specific performance from the information processing capacity of a reservoir computer.

In the reservoir computing literature, the information processing capacity is frequently used to characterize the computing capabilities of a reservoir. However, it remains unclear how the information processing capacity connects to the performance on specific tasks. We demonstrate on a set of standard benchmark tasks that the total information processing capacity correlates poorly with task specific performance.

Connecting reservoir computing with statistical forecasting and deep neural networks.

Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.

Reservoir Computing with Delayed Input for Fast and Easy Optimisation.

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence.

Deterministic and stochastic effects in spreading dynamics: A case study of bovine viral diarrhea.

Bovine viral diarrhea (BVD) is a disease in cattle with complex transmission dynamics that causes substantial economic losses and affects animal welfare. The infection can be transient or persistent. The mostly asymptomatic persistently infected hosts are the main source for transmission of the virus.

Stabilizing nanolasers via polarization lifetime tuning.

We investigate the emission dynamics of mutually coupled nanolasers and predict ways to optimize their stability, i.e., maximize their locking range.

How carrier memory enters the Haus master equation of mode-locking.

We present a generalization of the Haus master equation in which a dynamical boundary condition allows to describe complex pulse trains, such as the -switched and harmonic transitions of passive mode-locking, as well as the weak interactions between localized states. As an example, we investigate the role of group velocity dispersion on the stability boundaries of the -switched regime and compare our results with that of a time-delayed system.

Deep time-delay reservoir computing: Dynamics and memory capacity.

The deep time-delay reservoir computing concept utilizes unidirectionally connected systems with time-delays for supervised learning. We present how the dynamical properties of a deep Ikeda-based reservoir are related to its memory capacity (MC) and how that can be used for optimization. In particular, we analyze bifurcations of the corresponding autonomous system and compute conditional Lyapunov exponents, which measure generalized synchronization between the input and the layer dynamics.

Optical feedback induced oscillation bursts in two-state quantum-dot lasers.

We investigate the impact of short optical feedback on a two-state quantum dot laser. A region in the feedback parameter space is identified, where the laser emission periodically alternates between oscillation bursts from the quantum dot ground and excited state, i.e.

Performance boost of time-delay reservoir computing by non-resonant clock cycle.

The time-delay-based reservoir computing setup has seen tremendous success in both experiment and simulation. It allows for the construction of large neuromorphic computing systems with only few components. However, until now the interplay of the different timescales has not been investigated thoroughly.

Stochastic polarization switching induced by optical injection in bimodal quantum-dot micropillar lasers.

Mutual coupling and injection locking of semiconductor lasers is of great interest in non-linear dynamics and its applications for instance in secure data communication and photonic reservoir computing. Despite its importance, it has hardly been studied in microlasers operating at μW light levels. In this context, vertically emitting quantum dot micropillar lasers are of high interest.

Class-C semiconductor lasers with time-delayed optical feedback.

We perform a linear stability analysis and numerical bifurcation diagrams of a class-C laser with time-delayed optical feedback. We employ a rate equation system based on the Maxwell-Bloch equations, and study the influence of the dephasing time on the laser dynamics. We find a stabilizing effect of an intermediate dephasing time, i.

Mutual coupling and synchronization of optically coupled quantum-dot micropillar lasers at ultra-low light levels.

Synchronization of coupled oscillators at the transition between classical physics and quantum physics has become an emerging research topic at the crossroads of nonlinear dynamics and nanophotonics. We study this unexplored field by using quantum dot microlasers as optical oscillators. Operating in the regime of cavity quantum electrodynamics (cQED) with an intracavity photon number on the order of 10 and output powers in the 100 nW range, these devices have high β-factors associated with enhanced spontaneous emission noise.

Ultra-Short Pulse Generation in a Three Section Tapered Passively Mode-Locked Quantum-Dot Semiconductor Laser.

We experimentally and theoretically investigate the pulsed emission dynamics of a three section tapered semiconductor quantum dot laser. The laser output is characterized in terms of peak power, pulse width, timing jitter and amplitude stability and a range of outstanding pulse performance is found. A cascade of dynamic operating regimes is identified and comprehensively investigated.

Tailoring the mode-switching dynamics in quantum-dot micropillar lasers via time-delayed optical feedback.

Microlasers are ideal candidates to bring the fascinating variety of nonlinear complex dynamics found in delay-coupled systems to the realm of quantum optics. Particularly attractive is the possibility of tailoring the devices' emission properties via non-invasive delayed optical coupling. However, until now scarce research has been done in this direction.

Multipulse instabilities of a femtosecond SESAM-modelocked VECSEL.

Optically pumped passively modelocked vertical external-cavity surface-emitting lasers (VECSELs) can generate pulses as short as 100 fs with an intracavity semiconductor saturable absorber mirror (SESAM). Very stable soliton modelocking can be obtained, however, the high-Q-cavity, the short gain lifetime, and the kinetic-hole burning can also support rather complex multipulse instabilities which we analyze in more details here. This onset of multipulse operation limits the maximum average output power with fundamental modelocking and occurs at the roll-over of the cavity round trip reflectivity.

Bistability in two simple symmetrically coupled oscillators with symmetry-broken amplitude- and phase-locking.

In the model system of two instantaneously and symmetrically coupled identical Stuart-Landau oscillators, we demonstrate that there exist stable solutions with symmetry-broken amplitude- and phase-locking. These states are characterized by a non-trivial fixed phase or amplitude relationship between both oscillators, while simultaneously maintaining perfectly harmonic oscillations of the same frequency. While some of the surrounding bifurcations have been previously described, we present the first detailed analytical and numerical description of these states and present analytically and numerically how they are embedded in the bifurcation structure of the system, arising both from the in-phase and the anti-phase solutions, as well as through a saddle-node bifurcation.

Multipulse dynamics of a passively mode-locked semiconductor laser with delayed optical feedback.

Passively mode-locked semiconductor lasers are compact, inexpensive sources of short light pulses of high repetition rates. In this work, we investigate the dynamics and bifurcations arising in such a device under the influence of time delayed optical feedback. This laser system is modelled by a system of delay differential equations, which includes delay terms associated with the laser cavity and feedback loop.

Small chimera states without multistability in a globally delay-coupled network of four lasers.

We present results obtained for a network of four delay-coupled lasers modeled by Lang-Kobayashi-type equations. We find small chimera states consisting of a pair of synchronized lasers and two unsynchronized lasers. One class of these small chimera states can be understood as intermediate steps on the route from synchronization to desynchronization, and we present the entire chain of bifurcations giving birth to them.

Suppression of Noise-Induced Modulations in Multidelay Systems.

Many physical systems involve time-delayed feedback or coupling. In such delay systems, noise can give rise to undesirable oscillations at frequencies resonant to the delay times. We investigate how an additional feedback term can suppress noise-induced modulations in delay systems with self-feedback that exhibit deterministic oscillatory dynamics.