Quantum deep learning neural networks
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
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- Authors
- Kamruzzaman, A., Alhwaiti, Y., Leider, A., & Tappert, C. C.
- Affiliations
- Pace University (US)
- Corresponding author
- Kamruzzaman, Abu
- Venue
- Lecture notes in networks and systems
- Publisher
- Springer International Publishing
- Published
- 2019-02-02
- Pages
- 299-311
- ISSN
- 2367-3370 (ISSN-L)
- 2367-3389
- OpenAlex
- W2916082654
- Cited by
- 26
- References
- 27
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 2
- FWCI
- 1.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in networks and systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum neural networks, quantum deep learning, quantum computing hardware, quantum perceptron, nonlinear activation functions
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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