Quantum vs. Classical: A Rigorous Comparative Study on Neural Networks for Advanced Satellite Image Classification

Navigating the intersection of quantum computing and classical machine learning in image classification, this study confronts prevailing challenges. Centered on the "Satellite Image dataset (RSI-CB256)," our investigation probes the early phases of quantum architectures, utilizing simulations to transform numerical data into a quantum format, the investigation highlights the existing limitations in traditional classical methodologies for image classification tasks. In light of the groundbreaking possibilities presented by quantum computing, this study underscores the need for creative solutions to push image classification beyond the usual methods. Additionally, the study extends beyond conventional CNNs, incorporating Quantum Machine Learning through the Qiskit framework. This dualparadigm approach not only underscores the limitations of current classical machine learning methods but also sets the stage for a more profound understanding of the challenges that quantum methodologies aim to address. The research offers valuable insights into the ongoing evolution of quantum architectures and their potential impact on the future landscape of image classification and machine learning.