Abstract
Studies exploring the use of artificial intelligence (AI) and machine learning (ML) are knowing an undeniable success in many domains. On the other hand, quantum computing (QC) is an emerging field investigated by a large expanding research these last years. Its high computing performance is attracting the scientific community in search of computing power. Hybridizing ML with QC is a recent concern that is growing fast. In this paper, we are interested in quantum machine learning (QML) and more precisely in developing a quantum version of a density-based clustering algorithm namely, the Ordering Points To Identify the Clustering Structure (QOPTICS). The algorithm is evaluated theoretically showing that its computational complexity outperforms that of its classical counterpart. Furthermore, the algorithm is applied to cluster a large geographic zone with the aim to contribute in solving the problem of dispatching ambulances and covering emergency calls in case of COVID-19 crisis.
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Acknowledgement
We would like to express our special thanks of gratitude to Prince Mohammad Bin Fahd Center for Futuristic Studies for the support of this work.
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Drias, H., Drias, Y., Bendimerad, L.S., Houacine, N.A., Zouache, D., Khennak, I. (2022). Quantum Ordering Points to Identify the Clustering Structure and Application to Emergency Transportation. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_28
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