Memory-Augmented Hybrid Quantum Reservoir Computing

TL;DR

This paper introduces a hybrid quantum-classical reservoir computing model that simplifies implementation by using classical post-processing, demonstrating improved performance on chaotic time series prediction tasks across different physical platforms.

Contribution

It proposes a novel hybrid approach that avoids multiple coherent input injections in quantum reservoir computing, enhancing practicality and performance.

Findings

Achieved higher prediction steps on benchmark chaotic time series.

Validated on Ising model and Rydberg atom platforms.

Demonstrated improved predictive capabilities over previous methods.

Abstract

Reservoir computing (RC) is an effective method for predicting chaotic systems by using a high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning phase linear, which simplifies training and reduces computational complexity compared to fully trained recurrent neural networks (RNNs). Quantum reservoir computing (QRC) uses the exponential growth of Hilbert spaces in quantum systems, allowing for greater information processing, memory capacity, and computational power. However, the original QRC proposal requires coherent injection of inputs multiple times, complicating practical implementation. We present a hybrid quantum-classical approach that implements memory through classical post-processing of quantum measurements. This approach avoids the need for multiple coherent input injections and is evaluated on benchmark tasks, including the chaotic Mackey-Glass time series prediction. We tested our model on two physical platforms: a fully connected Ising model and a Rydberg atom array. The optimized model demonstrates promising predictive capabilities, achieving a higher number of steps compared to previously reported approaches.