Application of Quantum Machine Learning for Optimization and Data Imbalance Solutions

Mohanty, Nishikanta

Application of Quantum Machine Learning for Optimization and Data Imbalance Solutions

Mohanty, Nishikanta

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Mohanty, Nishikanta
dc.date.accessioned	2025-07-07T00:43:45Z
dc.date.available	2025-07-07T00:43:45Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/188096
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This thesis investigates the inventive application of quantum computing methods to solve two critical challenges: optimization challenges with a focus on the Vehicle Routing Problem (VRP) and the issue of data imbalance, especially via the Synthetic Minority Oversampling Technique (SMOTE). The first section of research delves into the VRP, a critical optimization challenge, and emphasizes the use of Quantum algorithms to solve the vehicle routing problem using hybrid quantum algorithms, as well as the effects of quantum noise. The research then extends to solve the VRP using a Quantum Machine Learning algorithm, the Quantum Support Vector Machine. The second portion of the research uses quantum approaches to address the class imbalance by reinterpreting the synthetic minority oversampling strategy in quantum. Using these cutting-edge research efforts, the thesis highlights quantum computing’s transformational potential for tackling complicated optimization issues and resolving data imbalance. It emphasizes the link between quantum physics and computer science, setting the path for future advances in quantum algorithms and their applications in various domains.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/188096/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Nishikanta Mohanty
dc.rights	au.edu.uts.lib/cph
dc.title	Application of Quantum Machine Learning for Optimization and Data Imbalance Solutions	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis investigates the inventive application of quantum computing methods to solve two critical challenges: optimization challenges with a focus on the Vehicle Routing Problem (VRP) and the issue of data imbalance, especially via the Synthetic Minority Oversampling Technique (SMOTE). The first section of research delves into the VRP, a critical optimization challenge, and emphasizes the use of Quantum algorithms to solve the vehicle routing problem using hybrid quantum algorithms, as well as the effects of quantum noise. The research then extends to solve the VRP using a Quantum Machine Learning algorithm, the Quantum Support Vector Machine. The second portion of the research uses quantum approaches to address the class imbalance by reinterpreting the synthetic minority oversampling strategy in quantum. Using these cutting-edge research efforts, the thesis highlights quantum computing’s transformational potential for tackling complicated optimization issues and resolving data imbalance. It emphasizes the link between quantum physics and computer science, setting the path for future advances in quantum algorithms and their applications in various domains.

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http://hdl.handle.net/10453/188096