Analysis of quantum machine learning algorithms in noisy channels for classification tasks in the IoT extreme environment

Satpathy, SK, Vibhu, V, Behera, BK, Al-Kuwari, S, Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149 and Farouk, A, 2023. Analysis of quantum machine learning algorithms in noisy channels for classification tasks in the IoT extreme environment. IEEE Internet of Things Journal. ISSN 2372-2541

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Abstract

By 2050, there will be a 50% rise in energy demand, and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively harvesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission rises. Extreme environmental cases will consequently lead to the imperfect functioning of IoT systems. By utilizing quantum-mechanical properties, such as superposition and entanglement, quantum computers can computationally outperform classical computers while consuming much less energy. In this paper, we investigate various quantum machine learning algorithms on two datasets (TWTDUS and SDWTT18) related to IoT extreme environment and study the effect of a noisy quantum environment. We observe that for the TWTDUS dataset, the variational UU† with analytical clustering methods achieves the highest accuracy of 98.10%. Similarly, for the SDWTT18 dataset, the UU† method with k-Means clustering achieves an accuracy of 94.43%. The results show that the accuracy of the proposed quantum algorithms outperforms the existing classical methods and can be utilized to forecast output power generation daily by measuring the metrics required in energy sector decision-making situations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decide instantly whether to start or stop generating units.

Item Type:	Journal article
Publication Title:	IEEE Internet of Things Journal
Creators:	Satpathy, S.K., Vibhu, V., Behera, B.K., Al-Kuwari, S., Mumtaz, S. and Farouk, A.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	1 August 2023
ISSN:	2372-2541
Identifiers:	Number Type 10.1109/jiot.2023.3300577 DOI 1788670 Other
Rights:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	04 Aug 2023 09:22
Last Modified:	04 Aug 2023 09:22
URI:	https://irep.ntu.ac.uk/id/eprint/49500

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