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

• URL: http://arxiv.org/abs/2602.21544v1
• Date: Wed, 25 Feb 2026 04:03:41 GMT
• Title: Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine
• Abstract summary: We propose a time-delayed quantum extreme learning machine (TD-QELM) for efficient time-series prediction on noisy quantum devices.<n>By encoding multiple past inputs simultaneously, TD-QELM achieves shallow circuit depth independent of sequence length.<n>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.
• Score: 0.7388859384645263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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