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 (SS) QRC and multistep (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 nontemporal 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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• Machine learning
• Quantum algorithms & computation
• Quantum information processing
• Rydberg atoms & molecules