Universal Approximation Theorem and Error Bounds for Quantum Neural Networks and Quantum Reservoirs
- DOI
- 10.1109/tnnls.2025.3552223
- Published
- 2025-06
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2025.3552223,
title = {Universal Approximation Theorem and Error Bounds for Quantum Neural Networks and Quantum Reservoirs},
author = {Lukas Gonon and Antoine Jacquier},
year = {2025},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2025.3552223},
url = {https://doi.org/10.1109/tnnls.2025.3552223}
}RIS
TY - JOUR TI - Universal Approximation Theorem and Error Bounds for Quantum Neural Networks and Quantum Reservoirs AU - Lukas Gonon AU - Antoine Jacquier PY - 2025 JO - IEEE Transactions on Neural Networks and Learning Systems DO - 10.1109/tnnls.2025.3552223 UR - https://doi.org/10.1109/tnnls.2025.3552223 ER -
APA
Gonon, L., & Jacquier, A. (2025). Universal Approximation Theorem and Error Bounds for Quantum Neural Networks and Quantum Reservoirs. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2025.3552223
Source records
- crossref · retrieved 2026-09-25T07:05:54.029Z