An Evaluation of Quantum Machine Learning Algorithms for Automating Quantum Circuit Design
Date
2025
Authors
Jian, Sicheng
Editors
Advisors
Sasdelli, Michele
Chin, Tat-Jun
Chin, Tat-Jun
Journal Title
Journal ISSN
Volume Title
Type:
Thesis
Citation
Statement of Responsibility
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Abstract
Quantum computation employing quantum gate-based circuits has demonstrated the potential to surpass classical computation. However, designing such circuits is challenging, unintuitive, and requires specialized knowledge in quantum physics. In this study, we investigate an underexplored domain: using machine learning to generate a single quantum circuit capable of performing specific functionalities based solely on given input-output data pairs. Due to the underexplored nature of this area, we experimentally evaluate various closely related algorithms that could be adapted to achieve this goal. We conclude with discussions that highlight future research directions to address this task.
School/Discipline
School of Computer and Mathematical Sciences
Dissertation Note
Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 2025
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