Combining a passive spatial photonic reservoir computer with a semiconductor laser increases its nonlinear computational capacity.

Photonic reservoir computing has been used to efficiently solve difficult and time-consuming problems. The physical implementations of such reservoirs offer low power consumption and fast processing speed due to their photonic nature. In this paper, we investigate the computational capacity of a passive spatially distributed reservoir computing system.

A recurrent Gaussian quantum network for online processing of quantum time series.

Over the last decade, researchers have studied the interplay between quantum computing and classical machine learning algorithms. However, measurements often disturb or destroy quantum states, requiring multiple repetitions of data processing to estimate observable values. In particular, this prevents online (real-time, single-shot) processing of temporal data as measurements are commonly performed during intermediate stages.

Transfer learning for photonic delay-based reservoir computing to compensate parameter drift.

Photonic reservoir computing has been demonstrated to be able to solve various complex problems. Although training a reservoir computing system is much simpler compared to other neural network approaches, it still requires considerable amounts of resources which becomes an issue when retraining is required. Transfer learning is a technique that allows us to re-use information between tasks, thereby reducing the cost of retraining.

Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning.

Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistical sampling makes them inefficient for training neural networks compared to digital computers. Here, we introduce a universal concept to achieve ultrafast statistical sampling with analog Ising machines by injecting noise.

Influence of the input signal's phase modulation on the performance of optical delay-based reservoir computing using semiconductor lasers.

In photonic reservoir computing, semiconductor lasers with delayed feedback have shown to be suited to efficiently solve difficult and time-consuming problems. The input data in this system is often optically injected into the reservoir. Based on numerical simulations, we show that the performance depends heavily on the way that information is encoded in this optical injection signal.