TL;DR
This paper reviews quantum machine learning methods for remote sensing, highlighting current advantages, challenges, and initial feasibility results, with plans to enhance quantum model complexity and output diversity.
Contribution
It provides an overview of quantum image classification techniques for remote sensing and discusses the bottlenecks on open source platforms, proposing future improvements.
Findings
Feasibility of quantum image classification demonstrated
Identified bottlenecks in current quantum algorithms
Plans to expand quantum hidden layers and output options
Abstract
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.
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