Nonlinear input transformations are ubiquitous in quantum reservoir computing
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 18, 2022
- Source ID
- 10.1088/2634-4386/ac4fcd
Entities
People
- G. E. Rowlands
- G. J. Ribeill
- Luke C. G. Govia
- T. A. Ohki
Organizations
- United States Army