Layer Expansion Techniques for Quantum Neural Networks in COVID-19 Image Classification Using Expressibility and Entangling Capability
DOI:
https://doi.org/10.13052/jmm1550-4646.2233Keywords:
Quantum computing, quantum neural network, layer expansion, quantum circuits, expressibility, entanglement capabilityAbstract
The COVID-19 pandemic claimed numerous lives, and led to the development of new tools for disease treatment. Initial diagnosis using X-rays can now identify COVID-19-infected individuals, and significant research has promoted neural networks for X-ray image classification. Quantum technology is also gaining interest, with intriguing properties such as superposition and entanglement. This study proposed a Quantum Neural Network (QNN) for X-ray image classification to investigate the relationship techniques between layer expansion and quantum circuits, measured by expressibility and entanglement capability. The results showed that both metrics significantly affected model accuracy. Seven circuits were tested, each with six layers, to examine their impact on model performance. The experiments yielded an accuracy of 95%, with highly effective image classification circuits exhibiting a balanced relationship between entanglement capability and expressibility.
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Sameer I. Ali Al-Janabi et al., “An Enhanced Convolutional Neural Network for COVID-19 Detection,” Intelligent Automation & Soft Computing, vol. 28, no. 2, pp. 293–303, 2021, doi:10.32604/iasc.2021.014419.
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, Apr. 2022, doi:10.1093/jcde/qwac003.
H. Allioui et al., “A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation,” J Pers Med, vol. 12, no. 2, 2022, doi:10.3390/jpm12020309.
M. Mahmood, W. J. Al-Kubaisy, and B. Al-Khateeb, “Review of IoT for COVID-19 Detection and Classification,” in International Conference on Innovative Computing and Communications, A. Khanna, D. Gupta, S. Bhattacharyya, A. E. Hassanien, S. Anand, and A. Jaiswal, Eds., Singapore: Springer Singapore, pp. 859–872, 2022.
I. F. Jassam, S. M. Elkaffas, and A. A. El-Zoghabi, “Chest X-Ray Pneumonia Detection by Dense-Net,” in 2021 31st International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, pp. 176–179, 2021, doi:10.1109/ICCTA54562.2021.9916637.
E. Çallı, E. Sogancioglu, B. van Ginneken, K. G. van Leeuwen, and K. Murphy, “Deep Learning for Chest X-Ray Analysis: A Survey,” Med Image Anal, vol. 72, p. 102125, 2021, doi: https://doi.org/10.1016/j.media.2021.102125.
S. Anand, R. K. Roshan, and D. Sundaram, “Chest X-Ray Image Enhancement using Deep Contrast Diffusion Learning,” Optik (Stuttg), vol. 279, p. 170751, 2023, doi: https://doi.org/10.1016/j.ijleo.2023.170751.
A. Leelasantitham, “A Novel Guideline Framework for Research and Development in the Field of Management of Technology and Innovation,” in 2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), pp. 1–5, 2024, doi:10.1109/TIMES-iCON61890.2024.10630775.
M. A. Yenikaya, G. Kerse, and O. Oktaysoy, “Artificial Intelligence in the Healthcare Sector: Comparison of Deep Learning Networks using Chest X-Ray Images,” Front Public Health, vol. 12, 2024, doi:10.3389/fpubh.2024.1386110.
Y. H. Bhosale and K. S. Patnaik, “Bio-Medical Imaging (X-ray, CT, ultrasound, ECG), Genome Sequences Applications of Deep Neural Network and Machine Learning in Diagnosis, Detection, Classification, and Segmentation of COVID-19: A Meta-analysis & Systematic Review,” Multimed Tools Appl, vol. 82, no. 25, pp. 39157–39210, 2023, doi:10.1007/s11042-023-15029-1.
P.K. Illa, T. Senthil Kumar, and F. Syed Anwar Hussainy, “Deep Learning Methods for Lung Cancer Nodule Classification: A Survey,” Journal of Mobile Multimedia vol. 18, pp. 421–450, 2022, https://doi.org/10.13052/jmm1550-4646.18213.
R. Idzikowski, M. A. Kucharski, K. Pempera, and M. Jaroszczuk, “A Survey on Quantum Machine Learning Applications in Medicine and Healthcare,” Multidisciplinary Digital Publishing Institute (MDPI), 1 Feb. 2026, doi:10.3390/app16031630.
D. Jain, S. Hundekari, K. Upreti, N. Jain, M. Rose, N. Singh, R. Singhai, and M. Kumar, “RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification,” Journal of Mobile Multimedia, vol. 20, pp. 1039–1066, 2024, https://doi.org/10.13052/jmm1550-4646.2053.
A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A Deep Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images,” Comput Methods Programs Biomed, vol. 196, Nov. 2020, doi:10.1016/j.cmpb.2020.105581.
R. K. Gupta et al., “Novel Deep Neural Network Technique for Detecting Environmental Effect of COVID-19,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, vol. 47, no. 1, pp. 8997–9015, 2025, doi:10.1080/15567036.2021.1941435.
S. Shomir Dutta, S. Sandeep, S. Sridevi, R. Mistry, J. Ortega, and G. Kar, “Novel Quanvolutional Neural Network-Based COVID-19 Diagnosis From Raman Spectroscopic Signals of Human Serum,” IEEE Access, vol. 13, pp. 104382–104405, 2025, doi:10.1109/ACCESS.2025.3580316.
W. El Maouaki, A. Marchisio, T. Said, M. Shafique, and M. Bennai, “Designing Robust Quantum Neural Networks via Optimized Circuit Metrics,” Advanced Quantum Technologies, 2025, https://doi.org/10.1002/qute.202400601.
S. Sim, P. D. Johnson, and A. Aspuru-Guzik, “Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms,” May 2019, doi:10.1002/qute.201900070.
T. Hubregtsen, J. Pichlmeier, P. Stecher, and K. Bertels, “Evaluation of Parameterized Quantum Circuits: On the Relation between Classification Accuracy, Expressibility, and Entangling Capability,” Quantum Mach Intell, vol. 3, no. 1, p. 9, 2021, doi:10.1007/s42484-021-00038-w.
M. M. Hossain, M. A. Rahim, A. N. Bahar, and M. M. Rahman, “Automatic Malaria Disease Detection from Blood Cell Images using the Variational Quantum Circuit,” Inform Med Unlocked, vol. 26, Jan. 2021, doi:10.1016/j.imu.2021.100743.
K. Sengupta and P. R. Srivastava, “Quantum Algorithm for Quicker Clinical Prognostic Analysis: An Application and Experimental Study using CT Scan Images of COVID-19 Patients,” BMC Med Inform Decis Mak, vol. 21, no. 1, Dec. 2021, doi:10.1186/s12911-021-01588-6.
Y. Zeng, H. Wang, J. He, Q. Huang, and S. Chang, “A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy,” Entropy, vol. 24, no. 3, 2022, doi:10.3390/e24030394.
H. Th. S. Alrikabi, I. A. Aljazaery, J. S. Qateef, A. H. M. Alaidi, and R. M. Al_airaji, “Face Patterns Analysis and Recognition System Based on Quantum Neural Network QNN,” International Journal of Interactive Mobile Technologies, vol. 16, no. 8, pp. 34-48, 2022, doi:10.3991/ijim.v16i08.30107.
T. Nguyen, I. Paik, Y. Watanobe, and T. C. Thang, “An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification,” Electronics (Basel), vol. 11, no. 3, 2022, doi: 10.3390/electronics11030437.
Y. Li, R. Liu, X. Hao, R. Shang, P. Zhao, and L. Jiao, “EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification,” Neural Networks, vol. 168, pp. 471–483, 2023, doi: https://doi.org/10.1016/j.neunet.2023.09.040.
A. Rana, D. K. Bhaglani, P. Vaidya, and Y. C. Hu, “Meta-Analysis of Quantum Convolutional Neural Networks for Automated Tuberculosis Screening on Chest X-Rays,” Multimed Tools Appl, vol. 84, pp. 40781–40807, 2025, https://doi.org/10.1007/s11042-025-20614-7.
A. Dubey, T. Gantala, A. Ray, A. Prabhakar, and P. Rajagopal, “Quantum Machine Learning for Recognition of Defects in Ultrasonic Imaging,” NDT & E International, vol. 150, p. 103262, 2025, doi: https://doi.org/10.1016/j.ndteint.2024.103262.
S. Rawas and D. AlSaeed, “Quantum Enhanced Machine Learning for Medical Image Analysis: A Hybrid Approach,” Applied Computing and Informatics, pp. 1–16, Jan. 2026, doi:10.1108/ACI-10-2025-0448.
S. C. Kak, “Quantum Neural Computing,” in Advances in Imaging and Electron Physics, vol. 94, pp. 259–313, 1995, doi: https://doi.org/10.1016/S1076-5670(08)70147-2.
R. Chrisley, “Quantum Learning,” in New Directions in Cognitive Science: Proceedings of the International Symposium, Saariselkä, Lapland, Finland, pp. 77–89, 1995.
M. Möttönen and J. Vartiainen, “Decompositions of General Quantum Gates,” Frontiers in Artificial Intelligence and Applications, May 2005.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum Machine Learning,” Nature, vol. 549, Nov. 2016, doi:10.1038/nature23474.
M. Schuld and F. Petruccione, Machine Learning with Quantum Computers, 2021, doi:10.1007/978-3-030-83098-4.
D. A. Meyer and N. R. Wallach, Global Entanglement in Multiparticle Systems, 2001.
R. Matsumoto and M. Hagiwara, “A Survey of Quantum Error Correction,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E104A, no. 12, pp. 1654–1664, 2021, doi:10.1587/transfun.2021EAI0001.
A. Chatterjee, K. Phalak, and S. Ghosh, “Quantum Error Correction For Dummies,” Quantum Physics, Apr. 2023, doi:10.1109/QCE57702.2023.00162.
N. Chaipunha, V. Panya, and A. Leelasantitham, “Analytical Challenges of Quantum and Classical Computing in Thailand: A Comparative Exploration of Machine Learning Through Classification,” in 2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), pp. 1–5, 2024, doi:10.1109/TIMES-iCON61890.2024.10630768.
