Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine

Abstract:We proposed a time-delayed quantum extreme learning machine (TD-QELM) for efficient time-series prediction on noisy intermediate-scale quantum (NISQ) devices. By encoding multiple past inputs simultaneously, TD-QELM achieves shallow circuit depth independent of sequence length, thereby, mitigating noise accumulation and reducing computational complexity. Experiments using the NARMA benchmark on both noiseless simulations and IBM's 127-qubit processor demonstrate that TD-QELM consistently outperforms conventional quantum reservoir computing in prediction accuracy and noise robustness. These results highlight TD-QELM as a practical and scalable framework for time-series learning on current NISQ hardware.

9 pages, 5 figures, 4 Tables. Corresponding author: Jun-ichi Shirakashi

arXiv:2602.21544 [quant-ph]

(or arXiv:2602.21544v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2602.21544 Focus to learn more arXiv-issued DOI via DataCite

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