Quantum Enhanced Image Analysis on IBM Hardware In NISQ-Era

Niranjala S. H.; M. Kazem Chamran; Mustafa Mowafak Alobaedy

doi:10.69968//ijisem.2026v5i2185-192

Authors

Niranjala S. H. Faculty of Information Technology, City University, Malaysia https://orcid.org/0009-0001-6580-4726
M. Kazem Chamran Faculty of Information Technology, City University, Malaysia https://orcid.org/0000-0003-3836-4443
Mustafa Mowafak Alobaedy Faculty of Information Technology, City University, Malaysia https://orcid.org/0000-0002-6562-2922

DOI:

https://doi.org/10.69968//ijisem.2026v5i2185-192

Keywords:

Machine learning, quantum machine learning, IBM Qiskit, image processing, NISQ, Variational Quantum Circuits

Abstract

The rapid growth of high-dimensional image data in the 21st century has revealed the inherent scalability limits of classical Convolutional Neural Network (CNN) models. The Moore’s Law nearing its physical and economic limits. As a result, use of energy latency involved in training multi-million-parameter models specially in applications such as medical imaging and satellite imaging. In this paper, a novel quantum-based architecture is proposed that incorporates Variational Quantum Circuits (VQCs) into a classical CNN. By mapping classical pixel information into a high-dimensional Hilbert space. Quantum entanglement is exploited to capture intricate spatial hierarchies with a fraction of the parameters of classical Euclidean-based models. We demonstrate a proof-of-concept of this novel framework using the IBM Quantum ecosystem and show that a Quantum-Enhanced CNN can reduce trainable parameters by 93% yet still attain accuracy levels that are competitive with state-of-the-art classical models. This paper provides a scalable proof of concept that paves the way for a "Quantum Utility" future in which data analysis is redefined in terms of quantum state space.

References

[1] M. S. Akter et al., "Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks Using a Malware Dataset: A Comparative Analysis," in 2023 IEEE International Conference on Software Services Engineering (SSE), Chicago, IL, USA: IEEE, Jul. 2023, pp. 222-231.

https://doi.org/10.1109/SSE60056.2023.00037

[2] Y. Ishiyama, R. Nagai, S. Mieda, Y. Takei, Y. Minato, and Y. Natsume, "Noise-robust optimization of quantum machine learning models for polymer properties using a simulator and validated on the IonQ quantum computer," Sci. Rep., vol. 12, no. 1, p. 19003, Nov. 2022

https://doi.org/10.1038/s41598-022-22940-4

[3] M. S. Akter et al., "Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection," in 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Torino, Italy: IEEE, Jun. 2023, pp. 1057-1063.

https://doi.org/10.1109/COMPSAC57700.2023.00161

[4] J. Alcazar, V. Leyton-Ortega, and A. Perdomo-Ortiz, "Classical versus Quantum Models in Machine Learning: Insights from a Finance Application," Jan. 08, 2020, arXiv: arXiv:1908.10778.

https://doi.org/10.1088/2632-2153/ab9009

[5] R. Alt, "On the potentials of quantum computing - An interview with Heike Riel from IBM Research," Electron. Mark., vol. 32, no. 4, pp. 2537-2543, Dec. 2022

https://doi.org/10.1007/s12525-022-00616-1

[6] S. Heshini Niranjala, M. Chamran, and M. Alobaedy, Evaluate Hybrid Classical-Quantum Architecture in Deep Image Processing. 2025, p. 242.

https://doi.org/10.1109/ICSET65917.2025.11284197

[7] F. Amato et al., "QuantuMoonLight: A low-code platform to experiment with quantum machine learning," SoftwareX, vol. 22, p. 101399, May 2023.

https://doi.org/10.1016/j.softx.2023.101399

[8] O. Ayoade, P. Rivas, and J. Orduz, "Artificial Intelligence Computing at the Quantum Level," Data, vol. 7, no. 3, p. 28, Feb. 2022

https://doi.org/10.3390/data7030028

[9] H. G. Enad and M. A. Mohammed, "Cloud computing-based framework for heart disease classification using quantum machine learning approach," J. Intell. Syst., vol. 33, no. 1, p. 20230261, Apr. 2024,

https://doi.org/10.1515/jisys-2023-0261

[10] S. Heshini Niranjala, S. B. Goyal, and A. Budati, "Secure and scalable data analysis framework with quantum machine learning and blockchain," 2025, pp.

https://doi.org/10.1201/9781003513445-17

[11] C. Ciliberto et al., "Quantum machine learning: a classical perspective," Proc. R. Soc. Math. Phys. Eng. Sci., vol. 474, no. 2209, p. 20170551, Jan. 2018

https://doi.org/10.1098/rspa.2017.0551

[12] H. Gupta, H. Varshney, T. K. Sharma, N. Pachauri, and O. P. Verma, "Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction," Complex Intell. Syst., vol. 8, no. 4, pp. 3073-3087, Aug. 2022

https://doi.org/10.1007/s40747-021-00398-7

[13] R. Bhavsar et al., "Classification of Potentially Hazardous Asteroids Using Supervised Quantum Machine Learning," IEEE Access, vol. 11, pp. 75829-75848, 2023

https://doi.org/10.1109/ACCESS.2023.3297498

[14] C. Cicconetti, M. Conti, and A. Passarella, "Resource Allocation in Quantum Networks for Distributed Quantum Computing," in 2022 IEEE International Conference on Smart Computing (SMARTCOMP), Jun. 2022, pp. 124-132.

https://doi.org/10.1109/SMARTCOMP55677.2022.00032

[15] F. F. Flöther, "The state of quantum computing applications in health and medicine," Res. Dir. Quantum Technol., pp. 1-21, Jul. 2023,

https://doi.org/10.1017/qut.2023.4

[16] A. Macaluso, L. Clissa, S. Lodi, and C. Sartori, "A Variational Algorithm for Quantum Neural Networks," in Computational Science - ICCS 2020, vol. 12142, V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, and J. Teixeira, Eds., in Lecture Notes in Computer Science, vol. 12142. , Cham: Springer International Publishing, 2020, pp. 591-604.

https://doi.org/10.1007/978-3-030-50433-5_45

[17] S. Heshini Niranjala, M. Alobaedy, and S. B. Goyal, "A Comparative Study of Machine Learning Techniques for Predicting Student Academic Performance," 2024, pp. 307-315.

https://doi.org/10.1007/978-3-031-73318-5_31

[18] H. Yang, X. Li, Z. Liu, and W. Pedrycz, "Improved Differential Privacy Noise Mechanism in Quantum Machine Learning," IEEE Access, vol. 11, pp. 50157-50164, 2023

https://doi.org/10.1109/ACCESS.2023.3274471

[19] M. Simonetti, D. Perri, and O. Gervasi, "Variational Methods in Optical Quantum Machine Learning," IEEE Access, vol. 11, pp. 131394-131408, 2023.

https://doi.org/10.1109/ACCESS.2023.3335625

[20] T. S. Humble, A. McCaskey, D. I. Lyakh, M. Gowrishankar, A. Frisch, and T. Monz, "Quantum Computers for High-Performance Computing," IEEE Micro, vol. 41, no. 5, pp. 15-23, Sep. 2021.

https://doi.org/10.1109/MM.2021.3099140

[21] O. Bouchmal, B. Cimoli, R. Stabile, J. J. Vegas Olmos, and I. Tafur Monroy, "From classical to quantum machine learning: survey on routing optimization in 6G software defined networking," Front. Commun. Netw., vol. 4, p. 1220227, Nov. 2023.

https://doi.org/10.3389/frcmn.2023.1220227

[22] G. Abdulsalam, S. Meshoul, and H. Shaiba, "Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach," Intell. Autom. Soft Comput., vol. 36, no. 1, pp. 761-779, 2023.

https://doi.org/10.32604/iasc.2023.032262

[23] Prajapat Sunil, Tomar Manish, P. Kumar, R. Kumar, and A. V. Vasilakos, "Quantum Computing Meets Deep Learning: A QCNN Model for Accurate and Efficient Image Classification," Mathematics, vol. 13, no. 19, p. 3148, 2025.

https://doi.org/10.3390/math13193148

[24] M. Cerezo, G. Verdon, H.-Y. Huang, L. Cincio, and P. J. Coles, "Challenges and Opportunities in Quantum Machine Learning," Nat. Comput. Sci., vol. 2, no. 9, pp. 567-576, Sep. 2022.

https://doi.org/10.1038/s43588-022-00311-3

[25] L. Chen et al., "Design and analysis of quantum machine learning: a survey," Connect. Sci., vol. 36, no. 1, p. 2312121, Dec. 2024.

https://doi.org/10.1080/09540091.2024.2312121