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.
@article{arxiv.2107.00147,
title = {Nonlinear input transformations are ubiquitous in quantum reservoir computing},
author = {L. C. G. Govia and G. J. Ribeill and G. E. Rowlands and T. A. Ohki},
journal= {arXiv preprint arXiv:2107.00147},
year = {2021}
}