Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices

TL;DR

This paper explores quantum reservoir computing, leveraging complex quantum dynamics for temporal machine learning on near-term quantum devices, and discusses related quantum machine learning methods with experimental feasibility.

Contribution

It introduces quantum reservoir computing and related approaches, demonstrating their practicality and effectiveness on current quantum hardware.

Findings

Quantum reservoir computing utilizes quantum dynamics for machine learning.

The methods are experimentally feasible on near-term quantum devices.

Quantum approaches outperform classical counterparts in certain tasks.

Abstract

Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. In this chapter, we explain quantum reservoir computing and related approaches, quantum extreme learning machine and quantum circuit learning, starting from a pedagogical introduction to quantum mechanics and machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.