  As medium-scale quantum computers progress, the application of quantum
algorithms across diverse fields like simulating physical systems, chemistry,
optimization, and cryptography becomes more prevalent. However, these quantum
computers, known as Noisy Intermediate Scale Quantum (NISQ), are susceptible to
noise, prompting the search for applications that can capitalize on quantum
advantage without extensive error correction procedures. Since, Machine
Learning (ML), particularly Deep Learning (DL), faces challenges due to
resource-intensive training and algorithmic opacity. Therefore, this study
explores the intersection of quantum computing and ML, focusing on computer
vision tasks. Specifically, it evaluates the effectiveness of hybrid
quantum-classical algorithms, such as the data re-uploading scheme and the
patch Generative Adversarial Networks (GAN) model, on small-scale quantum
devices. Through practical implementation and testing, the study reveals
comparable or superior performance of these algorithms compared to classical
counterparts, highlighting the potential of leveraging quantum algorithms in ML
tasks.
