2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 2 , PP: 61-72, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed Yousif 1 * , Belal Al-Khateeb 2
Doi: https://doi.org/10.54216/FPA.150205
In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.
Quantum computing, Quantum circuit , Convolutional Neural Network , COVID-19 , Quantum convolution , Quantum pooling , Quantum Convolutional Neural Network , Image classification.
[1] M. A. Mohammed, B. Al-Khateeb, M. Yousif, S. A. Mostafa, S. Kadry, and K. H. Abdulkareem, “Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model,” vol. 2022, 2022.
[2] E. H. Houssein, Z. Abohashima, M. Elhoseny, and W. M. Mohamed, “Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images,” J. Comput. Des. Eng., vol. 9, no. 2, pp. 343–363, 2022, doi: 10.1093/jcde/qwac003.
[3] H. Allioui, M. A. Mohammed, N. Benameur, B. Al-Khateeb, A. H. Abdulkareem, B. Garcia-Zapirain, R. Damaševičius, and R. Maskeliunas, “A Multi ‐ Agent Deep Reinforcement Learning Approach for Enhancement of COVID ‐ 19 CT Image Segmentation,” 2022.
[4] M. Mahmood, W. J. Al-kubaisy, and B. Al-Khateeb, Review of IoT for COVID-19 Detection. Springer Singapore. doi: 10.1007/978-981-16-3071-2.
[5] S. I. A. Al-janabi, B. Al-Khateeb, M. Mahmood, and B. Garcia-zapirain, “An Enhanced Convolutional Neural Network for COVID-19 Detection,” 2021, doi: 10.32604/iasc.2021.014419.
[6] V. Rajesh, U. P. Naik, and Mohana, “Quantum Convolutional Neural Networks (QCNN) Using Deep Learning for Computer Vision Applications,” 2021 6th Int. Conf. Recent Trends Electron. Information, Commun. Technol. RTEICT 2021, no. August 2021, pp. 728–734, 2021, doi: 10.1109/RTEICT52294.2021.9574030.
[7] T. M. Khan and A. Robles-Kelly, “Machine Learning: Quantum vs Classical,” IEEE Access, vol. 8, pp. 219275–219294, 2020, doi: 10.1109/ACCESS.2020.3041719.
[8] S. Sharma and P. Chaudhary, “Machine learning and deep learning,” Quantum Comput. Artif. Intell. Train. Mach. Deep Learn. Algorithms Quantum Comput., pp. 71–84, 2023, doi: 10.1515/9783110791402-004.
[9] G. Chen, Q. Chen, S. Long, W. Zhu, Z. Yuan, and Y. Wu, “Quantum convolutional neural network for image classification,” Pattern Anal. Appl., vol. 26, no. 2, pp. 655–667, 2023, doi: 10.1007/s10044-022-01113-z.
[10] A. Melnikov, M. Kordzanganeh, A. Alodjants, and R. K. Lee, “Quantum machine learning: from physics to software engineering,” Adv. Phys. X, vol. 8, no. 1, 2023, doi: 10.1080/23746149.2023.2165452.
[11] A. Zeguendry, Z. Jarir, and M. Quafafou, “Quantum Machine Learning: A Review and Case Studies,” Entropy, vol. 25, no. 2, pp. 1–41, 2023, doi: 10.3390/e25020287.
[12] S. S. Li, G. L. Long, F. S. Bai, S. L. Feng, and H. Z. Zheng, “Quantum computing,” Proc. Natl. Acad. Sci. U. S. A., vol. 98, no. 21, pp. 11847–11848, 2001, doi: 10.1073/pnas.191373698.
[13] P. Xu, Z. He, T. Qiu, and H. Ma, “Quantum image processing algorithm using edge extraction based on Kirsch operator,” Opt. Express, vol. 28, no. 9, p. 12508, 2020, doi: 10.1364/oe.386283.
[14] H. Sen Zhong et al., “Quantum computational advantage using photons,” Science (80-. )., vol. 370, no. 6523, pp. 1460–1463, 2020, doi: 10.1126/science.abe8770.
[15] T.-H. Q. Wei Li, Peng-Cheng Chu, Guang-Zhe Liu, Yan-Bing Tian and and S.-M. Wang, “An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network,” Quantum Eng., vol. 2022, p. 9, 2022, doi: https://doi.org/10.1155/2022/5701479.
[16] K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann, R. Wolf, “Training deep quantum neural networks,” Nat. Commun., vol. 11, no. 1, pp. 1–6, 2020, doi: 10.1038/s41467-020-14454-2.
[17] Y. Gujju, A. Matsuo, and R. Raymond, “Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications,” 2023, [Online]. Available: http://arxiv.org/abs/2307.00908
[18] Y. Jing et al., “RGB image classification with quantum convolutional ansatz,” Quantum Inf. Process., vol. 21, no. 3, pp. 1–19, 2022, doi: 10.1007/s11128-022-03442-8.
[19] S. Oh, J. Choi, and J. Kim, “A Tutorial on Quantum Convolutional Neural Networks (QCNN),” in 2020 International Conference on Information and Communication Technology Convergence (ICTC), 2020, vol. 2020-Octob, pp. 236–239. doi: 10.1109/ICTC49870.2020.9289439.
[20] L. H. Gong, J. J. Pei, T. F. Zhang, and N. R. Zhou, “Quantum convolutional neural network based on variational quantum circuits,” Opt. Commun., vol. 550, no. September 2023, p. 129993, 2024, doi: 10.1016/j.optcom.2023.129993.
[21] N. Mathur et al., “Medical image classification via quantum neural networks,” arXiv Prepr. arXiv2109.01831, pp. 1–14, 2021, [Online]. Available: http://arxiv.org/abs/2109.01831
[22] E. H. Houssein, Z. Abohashima, M. Elhoseny, and W. M. Mohamed, “Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images,” J. Comput. Des. Eng., vol. 9, no. 2, pp. 343–363, 2021, doi: 10.1093/jcde/qwac003.
[23] D. Arthur and P. Date, “A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification,” arXiv Prepr. arXiv2201.01820, 2022, doi: https://doi.org/10.48550/arXiv.2201.01820.
[24] E. Ovalle-Magallanes, J. G. Avina-Cervantes, I. Cruz-Aceves, and J. Ruiz-Pinales, “Hybrid classical–quantum Convolutional Neural Network for stenosis detection in X-ray coronary angiography,” Expert Syst. Appl., vol. 189, no. July 2021, p. 116112, 2022, doi: 10.1016/j.eswa.2021.116112.
[25] V. Kulkarni, S. Pawale, and A. Kharat, “A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs,” arXiv Prepr. arXiv2202.10452, pp. 1–15, 2022, [Online]. Available: http://arxiv.org/abs/2202.10452
[26] S. Farhan Ahmad, R. Rawat, and M. Moharir, “Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps,” Proc. 2nd IEEE Int. Conf. Comput. Intell. Knowl. Econ. ICCIKE 2021, pp. 345–349, 2021, doi: 10.1109/ICCIKE51210.2021.9410753.
