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Journal article
LHQNN: sequential and non-sequential layered hybrid quantum neural networks for image classification
Quantum machine intelligence, Vol.7(1), 51
2025
Abstract
Quantum neural networks have emerged as a promising approach to solving complex problems across various domains, especially when integrated with classical methods. Several hybrid quantum-classical architectures have been developed to leverage the potential of quantum advantages for image classification tasks. The design of the quantum layer plays an important role in exploiting quantum properties such as superposition and entanglement. In this research, we propose hybrid quantum neural networks with multiple quantum layers, utilizing sequential circuits for enhanced feature representation through structured depth, and non-sequential circuits to reduce complexity and improve performance. Our experimental results demonstrate that stacking multiple layers in the quantum circuit enhances performance significantly. Furthermore, the results indicate that the optimal range of 6–10 qubits achieves the best trade-off between accuracy and computational efficiency. The results also show that amplitude embedding consistently outperformed angle embedding for image classification tasks. Notably, our proposed hybrid sequential model with amplitude embedding outperforms traditional convolutional neural networks on MNIST and Fashion-MNIST datasets, while requiring fewer parameters. These findings provide valuable insights for advancing quantum machine learning in real-world applications.
Details
- Title
- LHQNN: sequential and non-sequential layered hybrid quantum neural networks for image classification
- Authors/Creators
- Monika Kabir - Murdoch UniversityMohammed Kaosar - Murdoch UniversityHamid Laga - Murdoch UniversityFerdous Sohel - School of Information Technology, Murdoch University
- Publication Details
- Quantum machine intelligence, Vol.7(1), 51
- Publisher
- Springer International Publishing
- Number of pages
- 15
- Grant note
- Murdoch University
- Identifiers
- 991005762520307891
- Copyright
- © The Author(s) 2025
- Murdoch Affiliation
- Centre for Biosecurity and One Health; Centre for Crop and Food Innovation; School of Information Technology; Centre for Healthy Ageing
- Language
- English
- Resource Type
- Journal article
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