Open Access
| Issue |
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence, Machine Learning & Data Analytics | |
| DOI | https://doi.org/10.1051/e3sconf/202672301001 | |
| Published online | 08 July 2026 | |
- H. Al-Sahaf, Y. Bi, Q. Chen, A. Lensen, Y. Mei, Y. Sun, B. Tran, B. Xue, M. Zhang, A survey on evolutionary machine learning, Journal of the Royal Society of New Zealand 49, 205 (2019). 10.1080/03036758.2019.1609052 [Google Scholar]
- P. Larrañaga, D. Atienza, J. Diaz-Rozo, A. Ogbechie, C. Puerto-Santana, C. Bielza, Industrial applications of machine learning (CRC Press, 2018) [Google Scholar]
- R.P. Feynman, in Feynman and computation (cRc Press, 2018), pp. 133–153 [Google Scholar]
- R. Rietsche, C. Dremel, S. Bosch, L. Steinacker, M. Meckel, J.M. Leimeister, Quantum computing, Electronic Markets 32, 2525 (2022). [Google Scholar]
- F. Bova, A. Goldfarb, R.G. Melko, Commercial applications of quantum computing, EPJ quantum technology 8, 2 (2021). [Google Scholar]
- J. Preskill, Quantum computing in the nisq era and beyond, Quantum 2, 79 (2018). 10.22331/q-2018-08-06-79 [CrossRef] [Google Scholar]
- X. Ge, R.B. Wu, H. Rabitz, The optimization landscape of hybrid quantum–classical algorithms: From quantum control to nisq applications, Annual Reviews in Control 54, 314 (2022). https://doi.org/10.1016/j.arcontrol.2022.06.001 [Google Scholar]
- E. Ovalle-Magallanes, J.G. Avina-Cervantes, I. Cruz-Aceves, J. Ruiz-Pinales, Hybrid classical–quantum convolutional neural network for stenosis detection in x-ray coronary angiography, Expert Systems with Applications 189, 116112 (2022). https://doi.org/10.1016/j.eswa.2021.116112 [Google Scholar]
- D. Ranga, S. Prajapat, Z. Akhtar, P. Kumar, A.V. Vasilakos, Hybrid quantum–classical neural networks for efficient mnist binary image classification, Mathematics 12 (2024). 10.3390/math12233684 [Google Scholar]
- F. Fan, Y. Shi, T. Guggemos, X.X. Zhu, Hybrid quantum-classical convolutional neural network model for image classification, IEEE Transactions on Neural Networks and Learning Systems 35, 18145 (2024). 10.1109/TNNLS.2023.3312170 [Google Scholar]
- Y. LeCun, C. Cortes, C. Burges, Mnist handwritten digit database, ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist 2 (2010). [Google Scholar]
- G. Cohen, S. Afshar, J. Tapson, A.V. Schaik, Emnist: Extending mnist to handwritten letters, 2017 International Joint Conference on Neural Networks (IJCNN) (2017). 10.1109/ijcnn.2017.7966217 [Google Scholar]
- P.A. Flanagan, NIST handprinted forms and characters NIST special database 19 (2016) [Google Scholar]
- Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: Analysis, applications, and prospects, IEEE Transactions on Neural Networks and Learning Systems 33, 6999 (2022). 10.1109/TNNLS.2021.3084827 [Google Scholar]
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., Pytorch: An imperative style, high-performance deep learning library (2019), 1912.01703, https://arxiv.org/abs/1912.01703 [Google Scholar]
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