Practical Quantum Reservoir Computing in Rydberg Atom Arrays

TL;DR

This study compares single-step and multi-step quantum reservoir computing architectures on Rydberg atom arrays, showing that single-step QRC is more robust and better suited for near-term quantum machine learning applications.

Contribution

The paper provides a comparative analysis of QRC architectures on Rydberg arrays, highlighting the robustness of SS-QRC over MS-QRC under realistic conditions.

Findings

MS-QRC performance is sensitive to dynamical phase and decoherence.

Sampling noise reduces MS-QRC's information processing capacity.

SS-QRC maintains high capacity and accuracy across tasks.

Abstract

Quantum reservoir computing (QRC) is a promising quantum machine learning framework for near-term quantum platforms, yet the performance of different QRC architectures under realistic constraints remains largely unexplored. Here, we provide a comparative numerical study of single-step-QRC (SS-QRC) and multi-step-QRC (MS-QRC) architectures implemented on a Rydberg atom array. We demonstrate that while MS-QRC performance is highly sensitive to the underlying dynamical phase of matter and decoherence, SS-QRC exhibits greater robustness. Using the randomized measurement toolbox to mitigate measurement overhead, we reveal that sampling noise undermines the convergence property required for MS-QRC. This leads to a significant reduction in the information processing capacity (IPC) of MS-QRC, deteriorating its performance on nonlinear time-series benchmarks. In contrast, SS-QRC maintains high IPC and accuracy across both temporal and non-temporal tasks. Our results suggest SS-QRC as a preferred candidate for near-term practical applications due to its resilience to system configurations and statistical noise.