International Journal on Science and Technology

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The Future of Breast Cancer Diagnosis: Benchmarking Quantum Machine Learning Models against Classical Techniques

Author(s) Ms. PARNAPALLI PUSHPANJALI, Dr. K Adisesha
Country India
Abstract Breast cancer continues to be among the most common causes of cancer death globally, with early and precise diagnosis playing a pivotal role in enhancing patient survival. Traditional machine learning (ML) techniques have shown great promise in the field of medical imaging and diagnosis; however, the emergence of quantum machine learning (QML) offers new avenues for the improvement of pattern discovery and diagnostic accuracy in high-dimensional medical data. This work describes a thorough benchmark comparison of quantum and classical machine learning methods for breast cancer diagnosis over the Wisconsin Diagnostic Breast Cancer data and mammographic image data. We compare variational quantum classifiers (VQC), quantum kernel methods (QKM), quantum convolutional neural networks (QCNNs), and hybrid quantum-classical neural structures with traditional classical baselines like support vector machines (SVM) and convolutional neural networks (CNN). QKM techniques perform better in high-dimensional feature spaces and better generalize on external validation sets. This implementation framework describes in depth how to develop, train, and deploy QML models in the clinical workflow, including optimization approaches, code structures, and deployment issues. The work demonstrates that it is possible to streamline breast cancer screening and diagnosis using QML with more accurate and more efficient solutions, which would lead to a huge impact on patient outcomes.
Keywords Quantum Machine Learning, Diagnosis of Breast Cancer, Medical Imaging, Variational Quantum Classifier, Quantum Kernel Methods, Hybrid Quantum-Classical Models
Field Computer Applications
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-09-16
DOI https://doi.org/10.71097/IJSAT.v16.i3.8236
Short DOI https://doi.org/g93xdd
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