Approaching limitations of digital computing technologies have spurred research in
neuromorphic and other unconventional approaches to computing. Here we argue that if we …

The combination of machine learning and quantum computing has emerged as a promising
approach for addressing previously untenable problems. Reservoir computing is an efficient …

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is
presented. RC is understood in the literature as the possibility of approximating input–output …

Reservoir computing approximation and generalization bounds are proved for a new
concept class of input/output systems that extends the so-called generalized Barron …

Most existing results in the analysis of quantum reservoir computing (QRC) systems with
classical inputs have been obtained using the density matrix formalism. This paper shows …

This paper shows that the celebrated embedding theorem of Takens is a particular case of a
much more general statement according to which, randomly generated linear state-space …

Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in
the literature often contradict well-established theoretical bounds. In this paper, we study the …

In recent years, the artificial intelligence community has seen a continuous interest in
research aimed at investigating dynamical aspects of both training procedures and machine …

This paper studies complexity measures of reservoir systems. For this purpose, a more
general model that we call a feature-based learning system, which is the composition of a …

Macroeconomic forecasting has recently started embracing techniques that can deal with
large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) …