Data Augmentations on Quantum Wasserstein Generative Adversarial Networks

dc.contributor.advisor	Jabeen, Shabnam
dc.contributor.author	Lee, Joey
dc.contributor.author	Lai, Devon
dc.contributor.author	Banerjee, Ayan
dc.date.accessioned	2024-12-17T15:31:18Z
dc.date.available	2024-12-17T15:31:18Z
dc.date.issued	2024-12-11
dc.description.abstract	The goal of this project is to explore Quantum Wasserstein Generative Adversarial Networks (QWGANs) and address its limitations by incorporating data augmentation techniques such as Elastic Transforms and Gaussian/Poisson Noise to simulate real-world imperfections, such as noise and distortions. With this we test the robustness of the QWGAN framework and compare QWGAN performance with such data modification techniques against one another
dc.identifier	https://doi.org/10.13016/md58-vmpa
dc.identifier.uri	http://hdl.handle.net/1903/33547
dc.language.iso	en_US
dc.relation.isAvailableAt	Department of Anthropology
dc.relation.isAvailableAt	College of Behavioral and Social Sciences
dc.relation.isAvailableAt	Digital Repository at the University of Maryland
dc.relation.isAvailableAt	University of Maryland (College Park, Md)
dc.subject	The First-Year Innovation & Research Experience (FIRE)
dc.subject	FIRE Quantum Machine Learning
dc.subject	quantum computing
dc.subject	machine learning
dc.title	Data Augmentations on Quantum Wasserstein Generative Adversarial Networks
dc.type	Other

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