mlsanityData to 7 Oct 2026

Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

arXiv 2101.10657 ↗First posted 26 Jan 2021quant-ph cs.AI cs.LGcited by 6

Not on the map. Why?

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 2 of its 16 references that are in the map. First posted on arXiv 26 Jan 2021.

Builds on

2 of 16 references.

References · 2

  1. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  2. 2017Machine learning & artificial intelligence in the quantum domaincited by 40

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 3; 2025: 0; 2026: 2.

6 papers.

  1. 2021On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classificationcited by 33
  2. 2024Classification and reconstruction for single-pixel imaging with classical and quantum neural networkscited by 2
  3. 2024Satellite image classification with neural quantum kernelscited by 2
  4. 2024On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observationcited by 1
  5. 2026Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classificationcited by 0
  6. 2026Hybrid Quantum CNN for Cross-Sensor Spaceborne Volcanic Thermal Activity Recognition Worldwidecited by 0