Boosting computational power through spatial multiplexing in quantum reservoir computing

TL;DR

This paper introduces a spatial multiplexing technique in quantum reservoir computing that enhances computational power by leveraging multiple small quantum systems in parallel, addressing experimental constraints and improving real-time signal processing capabilities.

Contribution

The paper proposes a novel spatial multiplexing scheme that exploits disjoint dynamics from multiple quantum systems to boost quantum reservoir computing performance under realistic experimental conditions.

Findings

Effective boost in computational power demonstrated through benchmark tasks.

Quantitative analysis of the technique's validity and limitations.

Potential for scalable quantum reservoir computing implementations.

Abstract

Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a computational resource. It can implement real-time signal processing and solve temporal machine learning problems in general, which requires memory and nonlinear mapping of the recent input stream using the quantum dynamics in computational supremacy region, where the classical simulation of the system is intractable. A nuclear magnetic resonance spin-ensemble system is one of the realistic candidates for such physical implementations, which is currently available in laboratories. In this paper, considering these realistic experimental constraints for implementing the framework, we introduce a scheme, which we call a spatial multiplexing technique, to effectively boost the computational power of the platform. This technique exploits disjoint dynamics, which originate from multiple different quantum systems driven by common input streams in parallel. Accordingly, unlike designing a single large quantum system to increase the number of qubits for computational nodes, it is possible to prepare a huge number of qubits from multiple but small quantum systems, which are operationally easy to handle in laboratory experiments. We numerically demonstrate the effectiveness of the technique using several benchmark tasks and quantitatively investigate its specifications, range of validity, and limitations in detail.