Quantum-Classical Hybrid Cancer Classification System

Author(s)	Mohammed Sufiyan, Anas Niaz, Saifuddin Syed, Syed Riyan
Country	India
Abstract	The integration of quantum computing with classical machine learning presents novel approaches to solving complex problems. This research focuses on developing a Quantum-Classical Hybrid Cancer Classification System using a quantum variational circuit for feature extraction and a classical SVM model for comparison. Breast cancer datasets were analyzed to demonstrate the potential of hybrid quantum-classical models in medical diagnostics. The results show that the quantum model achieves comparable performance to classical models with distinctive computational efficiency, offering a promising pathway for the future of quantum machine learning in healthcare.
Keywords	Quantum computing, Breast cancer classification, Hybrid quantum-classical systems, Variational quantum circuits, Machine learning, PennyLane, Support Vector Machines (SVM)
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-22
Cite This	Quantum-Classical Hybrid Cancer Classification System - Mohammed Sufiyan, Anas Niaz, Saifuddin Syed, Syed Riyan - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35632
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.35632
Short DOI	https://doi.org/g82wn2

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International Journal For Multidisciplinary Research