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.
@article{arxiv.2602.21544,
title = {Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine},
author = {Mio Kawanabe and Saud Cindrak and Kathy Luedge and Jun-ichi Shirakashi and Tetsuo Shibuya and Hiroshi Imai},
journal= {arXiv preprint arXiv:2602.21544},
year = {2026}
}