Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators' Software in an Industrial Context

TL;DR

It is demonstrated 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, and it is shown that the prediction quality of QUELL does not degrade when using fewer features.

Abstract

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.

Figures (9)

• Figure 1: Implementation of QELM
• Figure 2: Industrial Context of $\textsc{QUELL}$
• Figure 3: Overview of $\textsc{QUELL}$
• Figure 4: Two types of quantum encoders used in $\textsc{QUELL}$
• Figure 5: Two of the four quantum reservoirs used in $\textsc{QUELL}$