Comparison of Different Circuit Ansatz to Optimize Quantum Machine Learning Performance
Description
The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects of choosing different circuit ansatz and optimizers on the performance of a variational quantum classifier tasked with binary classification.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022-12
Agent
- Author (aut): Hsu, Brightan
- Thesis director: De Luca, Gennaro
- Committee member: Chen, Yinong
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Computer Science and Engineering Program