The FIRE Summit 2024

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    Data Augmentations on Quantum Wasserstein Generative Adversarial Networks
    (2024-12-11) Lee, Joey; Lai, Devon; Banerjee, Ayan; Jabeen, Shabnam
    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