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arXiv · 2602.00610

Practical Quantum Reservoir Computing in Rydberg Atom Arrays

Dong-Sheng Liu·Qing-Xuan Jie·Chang-Ling Zou·Xi-Feng Ren·Guang-Can Guo
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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.

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Dong-Sheng Liu, Qing-Xuan Jie, Chang-Ling Zou, Xi-Feng Ren, Guang-Can Guo. 2026-01-31. Practical Quantum Reservoir Computing in Rydberg Atom Arrays. https://doi.org/10.1103/pkhd-pl3w

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