# Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

Source: [Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing](<https://www.sota2.com/research/paper/68fa3f4e1e209131ce8ec420>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T04:09:14.353Z

Authors: Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo

Published: 2021-01-26T09:31:46.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.10657>)

[PDF](<https://arxiv.org/pdf/2101.10657.pdf>)