  We consider the problem of remotely tracking the state of and unstable linear
time-invariant plant by means of data transmitted through a noisy communication
channel from an algorithmic point of view. Assuming the dynamics of the plant
are known, does there exist an algorithm that accepts a description of the
channel's characteristics as input, and returns 'Yes' if the transmission
capabilities permit the remote tracking of the plant's state, 'No' otherwise?
Does there exist an algorithm that, in case of a positive answer, computes a
suitable encoder/decoder-pair for the channel? Questions of this kind are
becoming increasingly important with regards to future communication
technologies that aim to solve control engineering tasks in a distributed
manner. In particular, they play an essential role in digital twinning, an
emerging information processing approach originally considered in the context
of Industry 4.0. Yet, the abovementioned questions have been answered in the
negative with respect to algorithms that can be implemented on idealized
digital hardware, i.e., Turing machines. In this article, we investigate the
remote state estimation problem in view of the Blum-Shub-Smale computability
framework. In the broadest sense, the latter can be interpreted as a model for
idealized analog computation. Especially in the context of neuromorphic
computing, analog hardware has experienced a revival in the past view years.
Hence, the contribution of this work may serve as a motivation for a theory of
neuromorphic twins as a counterpart to digital twins for analog hardware.
