Quantum Reservoir Computing Implementations for Classical and Quantum Problems

TL;DR

This paper demonstrates how quantum reservoir computing, using quantum system dynamics, can improve machine learning tasks and quantum system modeling, outperforming classical neural networks especially with limited data.

Contribution

It introduces a quantum reservoir computing layer based on open quantum systems and benchmarks its performance against classical neural networks on image recognition and quantum dynamics tasks.

Findings

Quantum reservoir computing outperforms classical neural networks with larger datasets.

It requires fewer training epochs and smaller datasets for comparable accuracy.

Effective in modeling quantum system dynamics with limited training data.

Abstract

Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities. Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs to address computationally challenging tasks with enhanced efficiency and accuracy. To this end, we employ an open quantum system model comprising two-level atomic ensembles coupled to Lorentzian photonic cavities to construct a quantum physical reservoir computer layer for a recurrent neural network. We evaluate the effectiveness of this approach by applying it to a standard machine learning image-recognition problem and benchmarking its performance against a conventional neural network of similar architecture, but lacking the quantum physical reservoir computer layer. Remarkably, as the dataset size increases, the quantum physical reservoir computer outperforms the conventional neural network, requiring fewer training epochs and a smaller dataset to achieve comparable accuracy. Furthermore, we employ the quantum physical reservoir computing approach to model the dynamics of open quantum systems, focusing on atomic system ensembles interacting with a structured photonic reservoir associated with a photonic band-gap material. Our results reveal that the quantum reservoir computer provides equally powerful representations for quantum dynamical problems, maintaining effectiveness even under constraints of limited training data.