Exploring the power of quantum machine learning in the NISQ era

Yusen Wu

doi:10.26182/pbrn-z770

Exploring the power of quantum machine learning in the NISQ era

Yusen Wu

School of Physics, Mathematics and Computing

Research output: Thesis › Doctoral Thesis

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Abstract

Given the fundamental quantum-classical workflow in the context of the NISQ environment, this thesis endeavors to explore the performance of NISQ Quantum Machine Learning (QML) algorithms from the following perspectives. Firstly, it aims to identify practical applications where NISQ-QMLs may exhibit potential or provable quantum advantages. Secondly, it aims to develop resource efficient NISQ-QMLs for simulating molecular systems. Thirdly, it formulates a reasonable metric in characterizing whether a NISQ-QML algorithm is classically stimulable. Lastly, it aims to analyze the trainability of QAOA.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Wang, Jingbo, Supervisor Reynolds, Mark, Supervisor
Award date	28 Aug 2024
DOIs	https://doi.org/10.26182/pbrn-z770
Publication status	Unpublished - 2024

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10.26182/pbrn-z770

THESIS - DOCTOR OF PHILOSOPHY - WU Yusen - 2024
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3 Article

Orbital expansion variational quantum eigensolver
Wu, Y., Huang, Z., Sun, J., Yuan, X., Wang, J. B. & Lv, D., Oct 2023, In: Quantum Science and Technology. 8, 4, 23 p., 045030.
Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus)
Quantum Phase Recognition via Quantum Kernel Methods
Wu, Y., Wu, B., Wang, J. B. & Yuan, X., 2023, In: Quantum. 7, p. 981 1 p.
Research output: Contribution to journal › Article › peer-review

Open Access
27 Citations (Scopus)
Estimating Gibbs partition function with quantum Clifford sampling
Wu, Y. & Wang, J. B., Apr 2022, In: Quantum Science and Technology. 7, 2, 025006.
Research output: Contribution to journal › Article › peer-review
5 Citations (Scopus)

Cite this

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title = "Exploring the power of quantum machine learning in the NISQ era",

abstract = "Given the fundamental quantum-classical workflow in the context of the NISQ environment, this thesis endeavors to explore the performance of NISQ Quantum Machine Learning (QML) algorithms from the following perspectives. Firstly, it aims to identify practical applications where NISQ-QMLs may exhibit potential or provable quantum advantages. Secondly, it aims to develop resource efficient NISQ-QMLs for simulating molecular systems. Thirdly, it formulates a reasonable metric in characterizing whether a NISQ-QML algorithm is classically stimulable. Lastly, it aims to analyze the trainability of QAOA.",

keywords = "Quantum Machine Learning, ISQ Computation, Quantum Advantage",

author = "Yusen Wu",

year = "2024",

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language = "English",

school = "The University of Western Australia",

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N2 - Given the fundamental quantum-classical workflow in the context of the NISQ environment, this thesis endeavors to explore the performance of NISQ Quantum Machine Learning (QML) algorithms from the following perspectives. Firstly, it aims to identify practical applications where NISQ-QMLs may exhibit potential or provable quantum advantages. Secondly, it aims to develop resource efficient NISQ-QMLs for simulating molecular systems. Thirdly, it formulates a reasonable metric in characterizing whether a NISQ-QML algorithm is classically stimulable. Lastly, it aims to analyze the trainability of QAOA.

AB - Given the fundamental quantum-classical workflow in the context of the NISQ environment, this thesis endeavors to explore the performance of NISQ Quantum Machine Learning (QML) algorithms from the following perspectives. Firstly, it aims to identify practical applications where NISQ-QMLs may exhibit potential or provable quantum advantages. Secondly, it aims to develop resource efficient NISQ-QMLs for simulating molecular systems. Thirdly, it formulates a reasonable metric in characterizing whether a NISQ-QML algorithm is classically stimulable. Lastly, it aims to analyze the trainability of QAOA.

KW - Quantum Machine Learning

KW - ISQ Computation

KW - Quantum Advantage

U2 - 10.26182/pbrn-z770

DO - 10.26182/pbrn-z770

M3 - Doctoral Thesis

ER -

Exploring the power of quantum machine learning in the NISQ era

Abstract

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Research output

Orbital expansion variational quantum eigensolver

Quantum Phase Recognition via Quantum Kernel Methods

Estimating Gibbs partition function with quantum Clifford sampling

Cite this