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Publication Date
12-2024
Date of Final Oral Examination (Defense)
10-17-2024
Type of Culminating Activity
Dissertation - Boise State University Access Only
Degree Title
Master of Science in Computer Science
Department
Computer Science
Supervisory Committee Chair
Jun Zhuang, Ph.D.
Supervisory Committee Member
Grady Wright, Ph.D.
Supervisory Committee Member
Edoardo Serra, Ph.D.
Abstract
In recent years, Variational Quantum Circuit (VQC)s have been widely explored to advance quantum circuits against classic models on various domains, such as quantum chemistry and quantum machine learning. Similar to classic machine-learning models, VQCs can be optimized through gradient-based approaches. However, the gradient of VQCs may dramatically vanish during the optimization process as the number of qubits or layers increases. This issue, known as Barren Plateau (BP)s, seriously hinders the scaling of VQCs on large datasets. To mitigate the exponential gradient vanishing, extensive efforts have been devoted to tackling this issue through different methods. Some BP mitigation strategies include VQC parameter initialization and more complex VQC architectures such as Quantum Neural Network (QNN)s and Quantum Convolutional Neural Network (QCNN). In this thesis, we aim to investigate several circuit components through the lens of parameter initialization techniques. We study the performance and gradient variance of the different quantum circuit structures in combination with various initialization methods. In most existing studies, researchers typically focus on improving or understanding one aspect of the VQC approach. Instead, we plan to investigate how the two components of the VQC behave as we interchange the different variables of the circuit. This investigation will provide a better understanding of how the VQC structures behave under different initialization strategies for binary classification problems.
DOI
https://doi.org/10.18122/td.2305.boisestate
Recommended Citation
Cunningham, Jack, "Investigating Parameter Initialization Techniques in Variational Quantum Circuits" (2024). Boise State University Theses and Dissertations. 2305.
https://doi.org/10.18122/td.2305.boisestate