  The nascent computational paradigm of quantum reservoir computing presents an
attractive use of near-term, noisy-intermediate-scale quantum processors. To
understand the potential power and use cases of quantum reservoir computing, it
is necessary to define a conceptual framework to separate its constituent
components and determine their impacts on performance. In this manuscript, we
utilize such a framework to isolate the input encoding component of
contemporary quantum reservoir computing schemes. We find that across the
majority of schemes the input encoding implements a nonlinear transformation on
the input data. As nonlinearity is known to be a key computational resource in
reservoir computing, this calls into question the necessity and function of
further, post-input, processing. Our findings will impact the design of future
quantum reservoirs, as well as the interpretation of results and fair
comparison between proposed designs.
