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 …

Reservoir computation models form a subclass of recurrent neural networks with fixed non-
trainable input and dynamic coupling weights. Only the static readout from the state space …

A complete structure-preserving learning scheme for single-input/single-output (SISO) linear
port-Hamiltonian systems is proposed. The construction is based on the solution, when …

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 …

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) …

A universal kernel is constructed whose sections approximate any causal and time-invariant
filter in the fading memory category with inputs and outputs in a finite-dimensional Euclidean …

Quantum reservoir computing is an emergent field in which quantum dynamical systems are
exploited for temporal information processing. In previous work, it was found a feature that …