Comparing artificial neural networks with variational quantum circuits for biomedical data classification

Open Access

Issue		MATEC Web Conf. Volume 417, 2025 2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26^th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria


Article Number		02001
Number of page(s)		18
Section		Computational & Data-driven Modelling seminar
DOI		https://doi.org/10.1051/matecconf/202541702001
Published online		25 November 2025

1. J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd, Quantum machine learning. Nature 549, 195–202 (2017). [CrossRef] [PubMed] [Google Scholar]
C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, L. Wossnig, Quantum machine learning: a classical perspective. Proc. R. Soc. A 474, 20170551 (2018). [Google Scholar]
V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, J. M. Gambetta, Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019). [Google Scholar]
M. Schuld, A. Bocharov, K. M. Svore, N. Wiebe, Circuit-centric quantum classifiers. Phys. Rev. A 101, 032308 (2020). [Google Scholar]
K. Mitarai, M. Negoro, M. Kitagawa, K. Fujii, Quantum circuit learning. Phys. Rev. A 98, 032309 (2018). [Google Scholar]
M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini, Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4, 043001 (2019). [Google Scholar]
B. E. Yağcı, G. Demirsoy, A. N. Akpolat, General overview of artificial neural network applications in renewable energy systems. Turk. J. Electromech. Energy 9, 95–107 (2024). [Google Scholar]
A. Sequeira, L. P. Santos, L. S. Barbosa, On quantum natural policy gradients. IEEE Trans. Quantum Eng. 5, 1–11 (2024). [Google Scholar]
S. Anari, S. Sadeghi, G. Sheikhi, R. Ranjbarzadeh, M. Bendechache, Explainable attention-based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models. Sci. Rep. 15, 1027 (2025). [Google Scholar]
M. T. Ahmed, M. N. Imtiaz, A. Karmakar, Analysis of Wisconsin breast cancer original dataset using data mining and machine learning algorithms for breast cancer prediction. J. Sci. Technol. Environ. Inform. 9, 665–672 (2020). [Google Scholar]
P. Thirumalaikolundusubramanian, Comparison of Bayes classifiers for breast cancer classification. Asian Pac. J. Cancer Prev. 19, 2917 (2018). [Google Scholar]
V. Chaurasia, S. Pal, B. B. Tiwari, Prediction of benign and malignant breast cancer using data mining techniques. J. Algorithms Comput. Technol. 12, 119–126 (2018). [Google Scholar]
L. R. Borges, Analysis of the Wisconsin breast cancer dataset and machine learning for breast cancer detection. Group 1, 15–19 (1989). [Google Scholar]
N. Abdullayev, L. Aliyeva, J. Hasanov, Comparative study of quantum to classical machine learning algorithms for breast cancer classification, in Proc. 2023 IEEE 17th Int. Conf. on Application of Information and Communication Technologies (AICT), 1–6 (IEEE, 2023). [Google Scholar]
U. Desai, K. S. Kola, S. Nikhitha, G. Nithin, G. P. Raj, G. Karthik, Comparison of machine learning and quantum machine learning for breast cancer detection, in Proc. 2024 Int. Conf. on Smart Systems for Applications in Electrical Sciences (ICSSES), 1–6 (IEEE, 2024). [Google Scholar]
A. A. Borkowski, Classifying Breast Cancer with Quantum Machine Learning Using IBM Qiskit. [Online]. Available: https://tampapath.medium.com/classifying-breast-cancer-with-quantum-machine-learning-using-ibm-qiskit-c7c9f6575a00 (2023). [Google Scholar]
D. Dua, C. Graff, UCI Machine Learning Repository. [Online]. Available: http://archive.ics.uci.edu/ml (2017). [Google Scholar]
H. Hotelling, Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933). [CrossRef] [Google Scholar]

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