39 research outputs found
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
A new class of non-homogeneous state-affine systems is introduced for use in
reservoir computing. Sufficient conditions are identified that guarantee first,
that the associated reservoir computers with linear readouts are causal,
time-invariant, and satisfy the fading memory property and second, that a
subset of this class is universal in the category of fading memory filters with
stochastic almost surely uniformly bounded inputs. This means that any
discrete-time filter that satisfies the fading memory property with random
inputs of that type can be uniformly approximated by elements in the
non-homogeneous state-affine family.Comment: 41 page
Risk bounds for reservoir computing
We analyze the practices of reservoir computing in the framework of
statistical learning theory. In particular, we derive finite sample upper
bounds for the generalization error committed by specific families of reservoir
computing systems when processing discrete-time inputs under various hypotheses
on their dependence structure. Non-asymptotic bounds are explicitly written
down in terms of the multivariate Rademacher complexities of the reservoir
systems and the weak dependence structure of the signals that are being
handled. This allows, in particular, to determine the minimal number of
observations needed in order to guarantee a prescribed estimation accuracy with
high probability for a given reservoir family. At the same time, the asymptotic
behavior of the devised bounds guarantees the consistency of the empirical risk
minimization procedure for various hypothesis classes of reservoir functionals.Comment: 60 page
Simple Cycle Reservoirs are Universal
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 (reservoir) is trainable, thus avoiding the known
problems with propagation of gradient information backwards through time.
Reservoir models have been successfully applied in a variety of tasks and were
shown to be universal approximators of time-invariant fading memory dynamic
filters under various settings. Simple cycle reservoirs (SCR) have been
suggested as severely restricted reservoir architecture, with equal weight ring
connectivity of the reservoir units and input-to-reservoir weights of binary
nature with the same absolute value. Such architectures are well suited for
hardware implementations without performance degradation in many practical
tasks. In this contribution, we rigorously study the expressive power of SCR in
the complex domain and show that they are capable of universal approximation of
any unrestricted linear reservoir system (with continuous readout) and hence
any time-invariant fading memory filter over uniformly bounded input streams.Comment: 21 page
Gaussian states provide universal and versatile quantum reservoir computing
We establish the potential of continuous-variable Gaussian states in
performing reservoir computing with linear dynamical systems in classical and
quantum regimes. Reservoir computing is a machine learning approach to time
series processing. It exploits the computational power, high-dimensional state
space and memory of generic complex systems to achieve its goal, giving it
considerable engineering freedom compared to conventional computing or
recurrent neural networks. We prove that universal reservoir computing can be
achieved without nonlinear terms in the Hamiltonian or non-Gaussian resources.
We find that encoding the input time series into Gaussian states is both a
source and a means to tune the nonlinearity of the overall input-output map. We
further show that reservoir computing can in principle be powered by quantum
fluctuations, such as squeezed vacuum, instead of classical intense fields. Our
results introduce a new research paradigm for quantum reservoir computing and
the engineering of Gaussian quantum states, pushing both fields into a new
direction.Comment: 13 pages, 4 figure