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

TL;DR

The paper introduces a novel quantum machine learning model, TD-QELM, that efficiently predicts time-series data on NISQ devices by encoding multiple inputs simultaneously, reducing circuit depth and noise effects.

Contribution

It presents the time-delayed quantum extreme learning machine (TD-QELM), a new approach that improves prediction accuracy and noise robustness on NISQ hardware.

Findings

TD-QELM outperforms quantum reservoir computing in accuracy

Shallow circuit depth is achieved regardless of sequence length

Demonstrated effectiveness on IBM's 127-qubit processor

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.