Full metadata
Title
Measuring the use of dynamic circuits on performance metrics of Quantum Neural Networks
Description
The goal of this project is to measure the effects of the use of dynamic circuit technology within quantum neural networks. Quantum neural networks are a type of neural network that utilizes quantum encoding and manipulation techniques to learn to solve a problem using quantum or classical data. In their current form these neural networks are linear in nature, not allowing for alternative execution paths, but using dynamic circuits they can be made nonlinear and can execute different paths. We measured the effects of these dynamic circuits on the training time, accuracy, and effective dimension of the quantum neural network across multiple trials to see the impacts of the nonlinear behavior.
Date Created
2023-12
Contributors
- Lynch, Brian (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
Extent
18 pages
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2023-2024
Handle
https://hdl.handle.net/2286/R.2.N.190417
System Created
- 2023-11-28 03:20:33
System Modified
- 2023-11-29 05:26:57
- 11 months 1 week ago
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