Quantum Neural Networks: Architecture Design and Quantum Training

Liao, Yidong

Quantum Neural Networks: Architecture Design and Quantum Training

Liao, Yidong

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Liao, Yidong
dc.date.accessioned	2025-07-04T00:23:13Z
dc.date.available	2025-07-04T00:23:13Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/188077
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Quantum Neural Networks (QNNs) are promising machine learning models with potential quantum advantages over classical neural networks. This thesis focuses on their architecture design, training methodologies, and certain applications, addressing three challenges in QNN research: overcoming barren plateaus in training, designing problem-specific QNN models, and tackling state-of-the-art classical machine learning models. The thesis is divided into three main parts, each targeting a specific challenge. The first part proposes quantum-optimization-powered training methods that exploit hidden structures in the QNN optimization problem to mitigate the barren plateau issue. The second part designs problem-tailored QNNs for graph-structured data, incorporating inductive biases into their architectures to enhance trainability and gen- eralization. The third part explores the quantum implementation of Generative Pre-trained Transformers (GPT) — the original version of ChatGPT. By addressing these challenges, this thesis contributes to advancing the field of Quantum Machine Learning, offering new insights and methodologies for designing and training QNNs.	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/10453/188077/1/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	© 2025 Yidong Liao
dc.rights	au.edu.uts.lib/cph
dc.title	Quantum Neural Networks: Architecture Design and Quantum Training	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Quantum Neural Networks (QNNs) are promising machine learning models with potential quantum advantages over classical neural networks. This thesis focuses on their architecture design, training methodologies, and certain applications, addressing three challenges in QNN research: overcoming barren plateaus in training, designing problem-specific QNN models, and tackling state-of-the-art classical machine learning models. The thesis is divided into three main parts, each targeting a specific challenge. The first part proposes quantum-optimization-powered training methods that exploit hidden structures in the QNN optimization problem to mitigate the barren plateau issue. The second part designs problem-tailored QNNs for graph-structured data, incorporating inductive biases into their architectures to enhance trainability and gen- eralization. The third part explores the quantum implementation of Generative Pre-trained Transformers (GPT) — the original version of ChatGPT. By addressing these challenges, this thesis contributes to advancing the field of Quantum Machine Learning, offering new insights and methodologies for designing and training QNNs.

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http://hdl.handle.net/10453/188077