In a non-Hermitian spin reservoir on random graphs, the onset of the first exceptional point coincides with an abrupt jump in memory capacity, yielding a tunable learnability threshold.
Echo state networks are universal
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state-space model with a static linear readout map. This approximation is valid for infinite time intervals. The proof of this statement is based on fundamental results, also presented in this work, about the topological nature of the fading memory property and about reservoir computing systems generated by continuous reservoir maps.
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Dynamical learning and quantum memory with non-Hermitian many-body systems
In a non-Hermitian spin reservoir on random graphs, the onset of the first exceptional point coincides with an abrupt jump in memory capacity, yielding a tunable learnability threshold.