Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
It addresses the potential and challenges of applying quantum machine learning to remote sensing, but is incremental as it focuses on conceptual exploration and existing methods.
This concept paper outlines quantum image classification techniques for remote sensing and discusses bottlenecks in implementing them on current open-source platforms, with initial results showing feasibility.
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.