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Methods for accelerating geospatial data processing using quantum computers
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“It is worth to highlight that only very few works have addressed the application of quantum machine learning (QML) to remote sensing in the current state of the art.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [22]
Citations
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- National Academies of Sciences and Medicine (2019) Quantum Computing: Progress and Prospects. The National Academies Press, Washington
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On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
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Metadata
- Authors
- Henderson, M., Gallina, J., & Brett, M.
- Affiliations
- Rigetti Computing (United States) (US)
- Corresponding author
- Henderson, Max
- ORCID
- Henderson, Max
- Venue
- Quantum Machine Intelligence
- Published
- 2021-01-25
- Volume
- 3
- Issue
- 1
- ISSN
- 2524-4906 (ISSN-L)
- 2524-4914
- OpenAlex
- W3015863994
- Cited by
- 42
- References
- 20
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 6 · 2023: 9 · 2022: 6 · 2021: 7
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Quantum Machine Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- quantum machine learning, satellite image classification, geoprocessing, quantum advantage, convolutional neural networks, space industry, quantum computing
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