Multivariate quantum reservoir computing with discrete and continuous variable systems

TL;DR

This paper develops a framework for multivariate quantum reservoir computing, introducing new encoding schemes and a mixing capacity metric, and demonstrates the role of quantum effects in processing complex data.

Contribution

It presents a comprehensive multivariate processing framework, new encoding schemes, and links quantum properties to computational performance in quantum reservoirs.

Findings

Optimal encoding depends on the reservoir and task.

Peak performance aligns with non-classical quantum effects.

Quantum resources enhance multivariate data processing.

Abstract

Quantum reservoir computing is a promising paradigm for processing temporal data. So far, the primary focus has been on univariate time series. However, the most relevant and complex real-world data is multidimensional. In this paper, we establish an extensive framework for multivariate data processing in quantum reservoir computing. We propose and evaluate three multivariate encoding schemes and introduce the mixing capacity as a novel metric to evaluate the effectiveness with which a reservoir combines independent data streams. The computational performance of these proposed schemes is systematically assessed using this metric, as well as on the chaotic Lorenz-63 system prediction task, for two quantum reservoirs based on discrete and continuous-variable quantum systems. Furthermore, we relate the computational performance on these tasks to the underlying quantum properties of the reservoir. Our findings reveal that the optimal encoding method is highly dependent on the reservoir system and the specific task, underlining the importance of a task-specific input design. Moreover, we observe that peak computational performance coincides with the presence of non-classical effects, which indicates that quantum resources play a role in processing multivariate data.