← all papers · overview

Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

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.

Related papers

Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).