Lee, JoeyLai, DevonBanerjee, AyanThe 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 anotheren-USThe First-Year Innovation & Research Experience (FIRE)FIRE Quantum Machine Learningquantum computingmachine learningOther