Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many potential benefits, its real-world applications remain limited. To this end, we present QELM's industrial application in the context of elevators, by proposing an approach called QUELL. In QUELL, we use QELM for the waiting time prediction related to the scheduling software of elevators, with applications for software regression testing, elevator digital twins, and real-time performance prediction. The scheduling software has been implemented by our industrial partner Orona, a globally recognized leader in elevator technology. We demonstrate that QUELL can efficiently predict waiting times, with prediction quality significantly better than that of classical ML models employed in a state-of-the-practice approach. Moreover, we show that the prediction quality of QUELL does not degrade when using fewer features. Based on our industrial application, we further provide insights into using QELM in other applications in Orona, and discuss how QELM could be applied to other industrial applications.

翻译：量子极端学习机（QELM）是一种新兴技术，它利用量子动力学与易训练策略高效解决分类和回归等问题。尽管QELM具有诸多潜在优势，但其实际应用仍十分有限。为此，我们以电梯为工业应用场景，提出一种名为QUELL的方法。在QUELL中，我们将QELM应用于电梯调度软件的等待时间预测，该预测可服务于软件回归测试、电梯数字孪生以及实时性能预测。调度软件由我们的工业合作伙伴奥的斯（Orona）公司实现，该公司是全球公认的电梯技术领导者。我们证明QUELL能够高效预测等待时间，且其预测质量显著优于当前实践中最先进的经典机器学习模型。此外，我们展示了即使在使用较少特征的情况下，QUELL的预测质量也不会下降。基于该工业应用，我们进一步探讨了在奥的斯其他场景中使用QELM的可能性，并讨论了QELM如何应用于其他工业领域。