Natural quantum reservoir computing for temporal information processing

TL;DR

This paper explores using superconducting quantum devices as reservoirs for temporal data processing, leveraging natural quantum noise to outperform traditional models in regression and classification tasks.

Contribution

It introduces a novel approach of employing real quantum hardware as a reservoir, utilizing inherent noise as a computational resource for temporal information processing.

Findings

Quantum reservoir outperforms linear models in regression tasks.

Quantum reservoir achieves higher accuracy in object classification.

Quantum noise can be harnessed as a useful computational resource.

Abstract

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.