# Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

arXiv: https://arxiv.org/abs/2101.10657 · HTML: https://mlsanity.com/paper/2101.10657/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Machine learning & artificial intelligence in the quantum domain](https://mlsanity.com/paper/1709.02779/) (2017)

## Built on by (most cited first, 6 of 6)

- [On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification](https://mlsanity.com/paper/2109.09484/) (2021)
- [Classification and reconstruction for single-pixel imaging with classical and quantum neural networks](https://mlsanity.com/paper/2407.12506/) (2024)
- [Satellite image classification with neural quantum kernels](https://mlsanity.com/paper/2409.20356/) (2024)
- [On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation](https://mlsanity.com/paper/2410.08677/) (2024)
- [Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification](https://mlsanity.com/paper/2604.26675/) (2026)
- [Hybrid Quantum CNN for Cross-Sensor Spaceborne Volcanic Thermal Activity Recognition Worldwide](https://mlsanity.com/paper/2608.00069/) (2026)

## Abstract (arXiv)

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.