Quantum computing technology allows for more efficient problem-solving and provides essentially distinct answers to computational challenges when compared to classical computations. Machine learning algorithms have come to the fore in medical research and offer promising avenues for increasing the accuracy of cancer diagnosis. Breast cancer represents the majority of cancer cases in women worldwide, making it a serious and widespread public health issue. Improving patient outcomes requires early identification of cancer. The proposed Quantum Enhanced Prediction (QEP) project delves into the predictive power of two distinct learning paradigms: Support Vector Machine and Quantum Support Vector Machine, using Wisconsin breast cancer dataset. The research underscores the complexity of algorithms in cancer prediction. A departure from the ZZ feature map in QSVM marked the beginning of an intentional shift from traditional design. However, the study looked at the integration of the Z element map and Pauli matrices, providing the foundation for improved accuracy and a better grasp of quantum mechanics in cancer prediction. This conceptual shift intends to investigate the possible influence of quantum computing on breast cancer prediction with more granularity.
Quantum computing technology allows for more efficient problem-solving and provides essentially distinct answers to computational challenges when compared to classical computations. Machine learning algorithms have come to the fore in medical research and offer promising avenues for increasing the accuracy of cancer diagnosis. Breast cancer represents the majority of cancer cases in women worldwide, making it a serious and widespread public health issue. Improving patient outcomes requires early identification of cancer. The proposed Quantum Enhanced Prediction (QEP) project delves into the predictive power of two distinct learning paradigms: Support Vector Machine and Quantum Support Vector Machine, using Wisconsin breast cancer dataset. The research underscores the complexity of algorithms in cancer prediction. A departure from the ZZ feature map in QSVM marked the beginning of an intentional shift from traditional design. However, the study looked at the integration of the Z element map and Pauli matrices, providing the foundation for improved accuracy and a better grasp of quantum mechanics in cancer prediction. This conceptual shift intends to investigate the possible influence of quantum computing on breast cancer prediction with more granularity.
Breast cancer is a major health concern, and early detection can make a great difference in treatment and survival rates for breast cancer patients. Machine learning methods have noticeably improved prediction accuracy on high-dimensional medical datasets and have become widely used tools in medical diagnostics. The transition to a quantum computational framework has opened new directions in machine learning research. By using qubits instead of classical bits, quantum models may provide new ways to represent and process complex medical data. In medical diagnostics, this has led to a growing interest in whether quantum approaches can improve classification performance more effectively than classical methods. The purpose of this paper is to compare classical and quantum machine learning models on the task of breast cancer classification and to determine whether quantum models can achieve higher accuracy and faster prediction on a selected dataset. Alongside the main simulator-based experiment, a smaller experiment was performed on a real IBM quantum computer to demonstrate the practical execution of the same task under current hardware constraints. In the practical part of the study, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used. This dataset consists of 569 samples with 30 numerical features extracted from digitized fine needle aspirate images and provides a reliable basis for evaluating model performance. A comparative analysis was conducted between classical and quantum machine learning models, including a support vector machine (SVM), an artificial neural network (ANN), a quantum support vector machine (QSVM), and a hybrid quantum-classical neural network (QNN). In the simulator-based experiment, both SVM and ANN achieved an accuracy of 0.956 with an F1-score of 0.965 on the test set, while QSVM reached an accuracy of 0.807 with an F1-score of 0.866 and the Hybrid QNN achieved 0.623 accuracy with an F1-score of 0.677. These quantum and hybrid models also required substantially longer training times than the classical baselines. A small hardware experiment on an IBM quantum device further illustrates both the practical feasibility and the current limitations of executing this classification task on noisy intermediate-scale quantum hardware.
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.
The impacts of breast cancer remain a severe cause of death, significantly affecting the quality of life, especially among women. Traditional diagnostic approaches, particularly in the early stages, lack sophistication, reliability, and accuracy. Early and accurate diagnosis is crucial for improving survival rates and reducing the need for aggressive treatments. To address this, QSVM-GSA, an innovative quantum algorithm, is proposed. This novel integrated quantum framework simultaneously addresses the dual challenge of high diagnostic accuracy (via QSVM's quantum kernel trick) and computational efficiency/speed (via GSA integration). QSVM-GSA combines the Quantum Support Vector Machine and Grover's Search Algorithm, leveraging quantum mechanics for superior diagnostic accuracy and efficiency. It encodes complex medical data, such as the Wisconsin Breast Cancer Dataset, into quantum states. QSVM operates in the exponentially larger Hilbert space, identifying subtle patterns missed by classical classifiers. Quantum kernels establish nuanced relationships within the data, enabling precise tumour classification. Grover's search accelerates this process, offering a quadratic speed gain in scanning vast datasets and optimising feature selection. This allows for rapid identification of malignant patterns without compromising accuracy. Robustness is enhanced through advanced error-correcting techniques, ensuring fault tolerance and preserving computational integrity even in noisy environments. The model achieved 84.4% accuracy, 83.3% precision, 88.2% recall, and 85.7% F1-score on the Wisconsin Breast Cancer Dataset. Ultimately, QSVM-GSA represents a significant breakthrough in medical diagnostics, setting new standards in accuracy, speed, and reliability for breast cancer detection.
Abstract The breast cancer is widely prevalent, with millions of new cases and deaths reported annually. Globally, one in every 70 women is affected with breast cancer. It is apparent from the statistics that the number of breast cancer subjects are exponentially rising day by day. Early detection of the disease plays a crucial role to decrease the mortality rate and increase the survival chance. Quantum machine learning is an upcoming field in the era of big data, which helps to process the complex data more efficiently. This study aims to explore the QML models to accurately predict the breast cancer subjects and diagnose the disease in the early stage. Additionally, QML-based models aid in the classification of breast cancer as either benign or malignant. This study presents the comparative analysis of three QML models, namely QRF, QkNN and QSVM, for breast cancer classification using the Breast Cancer Wisconsin dataset. Each model is evaluated based on key performance metrics and the models achieved the accuracy of 97%, 94% and 90%, respectively. Hence, the results showed that the QRF outperformed QSVM and QkNN in classification accuracy, demonstrating superior ability to distinguish between malignant and benign tumors. This study highlights the potential of QML in medical diagnostics while emphasizing the need for further research in hybrid quantum-classical approaches, improved feature mapping techniques, and optimized quantum circuits.
Breast cancer is one of the most common neoplasms among women around the world, and early diagnosis significantly improves survival rates. Conventional techniques in the field of machine learning have shown great promise in the prediction of cancer, considering the fact that there is always an opportunity for advancement in terms of computing speed and scalability, especially when working with huge, high-dimensional data sets. In recent years, quantum computing (QC) has seemed to be becoming more prevalent as a powerful alternative. This study investigates the efficiency of traditional and quantum machine learning models in distinguishing between benign and malignant breast cancer tumors. The quantum model has achieved better balance in terms of precision and recall, especially in the case of malignant instance detection. These results demonstrate how QC can be used to supplement conventional machine learning techniques for improved predictive performance in breast cancer diagnosis, marking a significant step toward utilizing the technology’s potential in medicinal applications.
Quantum computing promises to revolutionize fields reliant on complex data processing, such as medical diagnostics. This paper presents an extended comparative analysis of classical and quantum machine learning (QML) techniques for breast cancer detection, using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Classical kernel-based Support Vector Machines (SVMs) and Quantum Support Vector Classification (QSVC) using IBM's Qiskit platform are employed. Beyond accuracy comparisons, this study provides insights into the technical challenges of quantum feature encoding, computational overheads, and hardware limitations. Results suggest that while classical models currently dominate, QML represents a credible future pathway for handling large-scale, complex medical datasets.