Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification
DOI:
https://doi.org/10.62411/faith.3048-3719-48Keywords:
Quantum Convolutional Neural Network (QCNN), Hybrid Quantum-Classical Model, Quantum Feature Encoding, Parameterized Quantum Gates, Iris DatasetAbstract
This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving
a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized
gate selection, and the integration of the quantum layer with classical neural network components.
This work contributes to the growing body of research on hybrid quantum-classical models and their
applicability to real-world datasets.
Downloads
References
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information. Cambridge University Press, 2012. doi: 10.1017/CBO9780511976667.
J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, Aug. 2018, doi: 10.22331/q-2018-08-06-79.
M. Schuld and F. Petruccione, “Quantum Machine Learning,” in Encyclopedia of Machine Learning and Data Mining, Boston, MA: Springer US, 2016, pp. 1–10. doi: 10.1007/978-1-4899-7502-7_913-1.
I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nat. Phys., vol. 15, no. 12, pp. 1273–1278, Dec. 2019, doi: 10.1038/s41567-019-0648-8.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, Sep. 2017, doi: 10.1038/nature23474.
V. Havlíček et al., “Supervised learning with quantum-enhanced feature spaces,” Nature, vol. 567, no. 7747, pp. 209–212, Mar. 2019, doi: 10.1038/s41586-019-0980-2.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” Phys. Rev. A, vol. 101, no. 3, p. 032308, Mar. 2020, doi: 10.1103/PhysRevA.101.032308.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Phys. Rev. A, vol. 98, no. 3, p. 032309, Sep. 2018, doi: 10.1103/PhysRevA.98.032309.
M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, “Parameterized quantum circuits as machine learning models,” Quantum Sci. Technol., vol. 4, no. 4, p. 043001, Nov. 2019, doi: 10.1088/2058-9565/ab4eb5.
E. Farhi and H. Neven, “Classification with Quantum Neural Networks on Near Term Processors,” arXiv. 2018. [Online]. Available: https://arxiv.org/abs/1802.06002
S. Lloyd, M. Schuld, A. Ijaz, J. Izaac, and N. Killoran, “Quantum embeddings for machine learning,” arXiv. Jan. 10, 2020. [Online]. Available: http://arxiv.org/abs/2001.03622
D. R. I. M. Setiadi, A. Susanto, K. Nugroho, A. R. Muslikh, A. A. Ojugo, and H. Gan, “Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model,” Computers, vol. 13, no. 8, p. 191, Aug. 2024, doi: 10.3390/computers13080191.
A. N. Safriandono, D. R. I. M. Setiadi, A. Dahlan, F. Z. Rahmanti, I. S. Wibisono, and A. A. Ojugo, “Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification,” J. Futur. Artif. Intell. Technol., vol. 1, no. 1, pp. 51–63, Jun. 2024, doi: 10.62411/faith.2024-12.
A. W. Harrow and A. Montanaro, “Quantum computational supremacy,” Nature, vol. 549, no. 7671, pp. 203–209, Sep. 2017, doi: 10.1038/nature23458.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, doi: 10.1109/5.726791.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in arXiv, Sep. 2014, pp. 1–14. [Online]. Available: https://arxiv.org/abs/1409.1556
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in arXiv, Dec. 2015, vol. 2016-Decem, pp. 770–778. doi: 10.1109/CVPR.2016.90.
J. Qiu et al., “Going Deeper with Embedded FPGA Platform for Convolutional Neural Network,” in Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Feb. 2016, pp. 26–35. doi: 10.1145/2847263.2847265.
S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in arXiv, Feb. 2015, pp. 448–456. [Online]. Available: http://arxiv.org/abs/1502.03167
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. London, England: MIT Press, 2016.
M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in Computer Vision – ECCV 2014, 2014, pp. 818–833. doi: 10.1007/978-3-319-10590-1_53.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 S.M. Yousuf Iqbal Tomal, Abdullah Al Shafin, Afrida Afaf, Debojit Bhattacharjee
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
