The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.
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
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472
Quantum Support Vector Machine for Big Data Classification
Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789
Quanvolutional neural networks: powering image recognition with quantum circuits
Henderson, M., Shakya, S., Pradhan, S. M., & Cook, T. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00012-y · Cited by 584
Self-citationFive Balltree Construction Algorithms
Omohundro, S. M. (2009) · Cited by 413
64th International Astronautical Congress 2013 (IAC 2013)
Berthoud, L., Schroeven-Deceuninck, H., Vrublevskis, J., Guest, M., Smith, C. L., Grady, M. M., & Bridges, J. C. (2014) · Cited by 338
Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., & Nemani, R. (2015) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2820783.2820816 · Cited by 313
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab859 · Cited by 274
Research works printed but not linked: 2
- Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, pp 265–283
- National Academies of Sciences and Medicine (2019) Quantum Computing: Progress and Prospects. The National Academies Press, Washington
Cited by
Sorted by citations
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162
Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey
Dritsas, E., & Τρίγκα, Μ. (2025) · Remote Sensing · doi:10.3390/rs17030550 · Cited by 203
Quantum computing challenges in the software industry. A fuzzy AHP-based approach
Awan, U., Hannola, L., Tandon, A., Goyal, R. K., & Dhir, A. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.106896 · Cited by 163
Implementation of Quantum Annealing: A Systematic Review
Yulianti, L. P., & Surendro, K. (2022) · IEEE Access · doi:10.1109/access.2022.3188117 · Cited by 68
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)
Alam, M., Kundu, S., Topaloglu, R. O., & Ghosh, S. (2021) · International Conference on Computer Aided Design · doi:10.1109/iccad51958.2021.9643516 · Cited by 63
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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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Methods for accelerating geospatial data processing using quantum computers. https://app.cochat.ai/papers/10.1007/s42484-020-00034-6
- The paper
- Henderson, M., Gallina, J., & Brett, M. (2021). Methods for accelerating geospatial data processing using quantum computers. Quantum Machine Intelligence. https://doi.org/10.1007/s42484-020-00034-6
