Nonlinear input transformations are ubiquitous in quantum reservoir computing

TL;DR

This paper investigates the role of nonlinear input transformations in quantum reservoir computing, revealing that input encoding often inherently provides nonlinearity, which influences future design and evaluation of quantum reservoirs.

Contribution

It provides a framework to analyze quantum reservoir computing components, highlighting the importance of nonlinear input encoding and questioning the need for additional processing.

Findings

Most quantum reservoir schemes use nonlinear input encoding.

Nonlinear input encoding may suffice for computational power in reservoir computing.

Implications for designing and comparing quantum reservoir systems.

Abstract

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.