  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.
