Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Abstract
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
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BibTeX
@misc{monbroussou2026hybrid,
title = {{Hybrid Quantum Neural Networks: Theory, Implementations, and Applications}},
author = {Léo Monbroussou and Maniraman Periyasamy and Viacheslav Kuzmin and Pavel Sekatski and Viktoria Patapovich and Asel Sagingalieva and Alexey Melnikov},
howpublished = {HAL},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.01194},
eprint = {2608.01194},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05731950v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
AU - Léo Monbroussou
AU - Maniraman Periyasamy
AU - Viacheslav Kuzmin
AU - Pavel Sekatski
AU - Viktoria Patapovich
AU - Asel Sagingalieva
AU - Alexey Melnikov
PY - 2026
DA - 2026/08/31
PB - HAL
DO - 10.48550/arxiv.2608.01194
UR - https://hal.science/hal-05731950v1
AB - Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
ER -