Full metadata
Title
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
2022-12
Contributors
- Hsu, Brightan (Author)
- De Luca, Gennaro (Thesis director)
- Chen, Yinong (Committee member)
- Barrett, The Honors College (Contributor)
- Computer Science and Engineering Program (Contributor)
Topical Subject
Resource Type
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2022-2023
Handle
https://hdl.handle.net/2286/R.2.N.170915
System Created
- 2022-12-03 12:57:44
System Modified
- 2022-12-09 11:49:41
- 1 year 11 months ago
Additional Formats