This repository contains an implementation of a hybrid quantum convolutional neural network (CNN) for satellite image data classification. The hybrid model combines classical deep learning techniques using PyTorch with quantum computing techniques using Qiskit to improve the accuracy and efficiency of satellite image classification tasks.
Satellite images provide valuable information for various applications, including land cover classification, urban planning, disaster monitoring, and environmental studies. Convolutional neural networks (CNNs) have shown great success in analyzing and extracting meaningful features from satellite images. However, traditional CNNs are limited by their computational power and may struggle with complex and high-dimensional datasets.
Quantum computing, on the other hand, offers potential advantages for image classification tasks due to its ability to handle large-scale computations and exploit quantum phenomena such as entanglement and superposition. By combining classical deep learning techniques with quantum computing, we can leverage the strengths of both approaches and achieve improved performance in satellite image classification.
The individuals responsible, deserve credit for their significant project