ARCHIVES

1. Q. Bai and X. Hu, “Superposition-enhanced quantum neural network for multi-class image classification,” Chinese Journal of Physics,
vol. 89, pp. 378–389, 2024, doi: 10.1016/j.cjph.2024.03.026.
2. A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre, “Data re-uploading for a universal quantum classifier,” Quantum,
vol. 4, Art. no. 226, 2020, doi: 10.22331/q-2020-02-06-226.
3. F. Fan, Y. Shi, T. Guggemos, and X. X. Zhu, “Hybrid quantum-classical convolutional neural network model for image classification,”
IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 12, pp. 18145–18159, Dec. 2024, doi:
10.1109/TNNLS.2023.3312170.
4. D. Bokhan, A. S. Mastiukova, A. S. Boev, D. N. Trubnikov, and A. K. Fedorov, “Multiclass classification using quantum convolutional
neural networks with hybrid quantum-classical learning,” Frontiers in Physics, vol. 10, Art. no. 1069985, 2022, doi:
10.3389/fphy.2022.1069985.
5. M. Kabir, M. Kaosar, H. Laga, and F. Sohel, “LHQNN: Sequential and non-sequential layered hybrid quantum neural networks for
image classification,” Quantum Machine Intelligence, vol. 7, Art. no. 51, 2025, doi: 10.1007/s42484-025-00279-z.
6. S. M. A. Rizvi, U. I. Paracha, U. Khalid, K. Lee, and H. Shin, “Quantum machine learning: Towards hybrid quantum-classical vision
models,” Mathematics, vol. 13, no. 16, Art. no. 2645, 2025, doi: 10.3390/math13162645.
7. C. Ding, S. Wang, Y. Wang, and W. Gao, “Quantum machine learning for multiclass classification beyond kernel methods,” Physical
Review A, vol. 111, no. 6, Art. no. 062410, 2025, doi: 10.1103/PhysRevA.111.0624108. D. Basilewitsch, J. F. Bravo, C. Tutschku, and F. Struckmeier, “Quantum neural networks in practice: A comparative study with
classical models from standard data sets to industrial images,” Quantum Machine Intelligence, vol. 7, Art. no. 110, 2025, doi:
10.1007/s42484-025-00336-7.
9. A. Mukhanbet and B. Daribayev, “A hybrid quantum–classical architecture with data re-uploading and genetic algorithm optimization
for enhanced image classification,” Computation, vol. 13, no.8, Art. no. 185, 2025, doi: 10.3390/computation13080185.
10. S. Anwar, S. Das, M. I. Hossain, and J. Mahmud, “Hybrid quantum-classical learning for multiclass image classification,” arXiv
preprint arXiv:2508.18161, 2025, doi: 10.48550/arXiv.2508.18161.
11. M. Das and T. Bolisetti, “Variational quantum neural networks (VQNNs) in image classification,” arXiv preprint arXiv:2303.05860,
2023, doi: 10.48550/arXiv.2303.05860.
12. D. Ranga, S. Prajapat, Z. Akhtar, P. Kumar, and A. V. Vasilakos, “Hybrid quantum–classical neural networks for efficient MNIST
binary image classification,” Mathematics, vol. 12, no. 23, Art. no. 3684, 2024, doi: 10.3390/math12233684.
13. [13] N. Schetakis, P. Bonfini, N. Alisoltani, K. Blazakis, S. I. Tsintzos, A. Askitopoulos, D. Aghamalyan, P. Fafoutellis, and E. I.
Vlahogianni, “Quantum neural networks with data re-uploading for urban traffic time series forecasting,” Scientific Reports, vol. 15,
Art. no. 19400, 2025, doi: 10.1038/s41598-025-04546-8.
14. S. M. Y. I. Tomal, A. Al Shafin, A. Afaf, and D. Bhattacharjee, “Quantum convolutional neural network: A hybrid quantum-classical
approach for Iris dataset classification,” Journal of Future Artificial Intelligence and Technologies, vol. 1, no. 3, pp. 284–295, 2024,
doi: 10.62411/faith.3048-3719-48.
15. D. Deepika, K. Vanishree, and M. Krishna, “A hybrid quantum–classical convolutional neural network for enhanced image
classification,” International Journal of Computer Applications, vol. 187, no. 66, pp. 28–34, 2025, doi: 10.5120/ijca2025926113.
16. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE,
vol. 86, no. 11, pp. 2278–2324, 1998, doi: 10.1109/5.726791.
17. H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms,”
arXiv:1708.07747, 2017, doi: 10.48550/arXiv.1708.07747.