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.

Drias, H., Drias, Y., Bendimerad, L., Houacine, N., Zouache, D., Khennak, I. (2022). Quantum Ordering Points to Identify the Clustering Structure and Application to Emergency Transportation. In Intelligent Systems Design and Applications 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021) Held During December 13–15, 2021 (pp.306-315). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-96308-8_28].

Quantum Ordering Points to Identify the Clustering Structure and Application to Emergency Transportation

Drias Y.;
2022

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.
paper
Application; OPTICS algorithm; Quantum machine learning; Quantum OPTICS; Quantum procedures; Unsupervised clustering;
English
21st International Conference on Intelligent Systems Design and Applications, ISDA 2021 - 13 December 2021 through 15 December 2021
2021
Ajith Abraham, Niketa Gandhi, Thomas Hanne, Tzung-Pei Hong, Tatiane Nogueira Rios, Weiping Ding
Intelligent Systems Design and Applications 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021) Held During December 13–15, 2021
9783030963071
2022
418 LNNS
306
315
none
Drias, H., Drias, Y., Bendimerad, L., Houacine, N., Zouache, D., Khennak, I. (2022). Quantum Ordering Points to Identify the Clustering Structure and Application to Emergency Transportation. In Intelligent Systems Design and Applications 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021) Held During December 13–15, 2021 (pp.306-315). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-96308-8_28].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/506759
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