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.
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.
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.
Ovarian cancer remains one of the leading causes of cancer-related mortality among women worldwide, largely because most cases are diagnosed at advanced stages. Current diagnostic tools and classical machine learning models often show limited sensitivity and specificity, particularly when applied to the complex, high-dimensional datasets required for accurate prediction. These limitations highlight the need for more powerful computational approaches capable of extracting subtle diagnostic patterns. The aim of this study is to enhance the accuracy and efficiency of ovarian cancer diagnosis by using quantum-inspired machine learning approach such as Quantum Support Vector Machines (QSVMs). The study utilized QSVMs to analyse clinical and genomic datasets, leveraging quantum mechanics principles like superposition and entanglement to process data more effectively than traditional machine learning models. The QSVM model was developed, trained, and evaluated using performance metrics such as accuracy, sensitivity, specificity, and processing efficiency. The results demonstrated that QSVMs achieved a diagnostic accuracy of 92%, outperforming traditional support vector machines and other classical models. Additionally, the processing time for diagnosis was reduced from 45 minutes to 20 minutes, providing a faster and more reliable workflow. The QSVMs also excelled in analysing multi-omics data, enabling the identification of early-stage ovarian cancer biomarkers and supporting personalized treatment strategies. In conclusion, this study demonstrates the potential of QSVMs to transform ovarian cancer diagnostics by addressing key limitations of conventional methods. The findings underscore the importance of adopting quantum-inspired machine learning in medical applications and encourage further exploration of these advanced algorithms in improving healthcare outcomes.
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.
The application of machine learning has shown significant progress in various fields, including clinical data classification. This study presents a comparative analysis between the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) for breast cancer classification using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Principal Component Analysis (PCA) was applied as dimension reduction strategy across three configurations (PCA = 2, PCA = 4, and PCA = 6) to evaluate the sensitivity of both models to the feature space dimension. Preprocessing included log1p transformation on 22 skewed features, Manual SMOTE balancing, and MinMaxScaler normalization. SVM was optimized via GridSearchCV (80 parameter combinations, cv = 5), while QSVM employed a ZZFeatureMap quantum kernel (reps = 2, entanglement = full) on qasm_simulator. Experimental results demonstrate that SVM achieved superior accuracy across all configurations, with peak performance at PCA = 6 (Accuracy= 98.83%, AUC = 0.9985, Specificity = 1.0000, Sensitivity = 0.9688, Precision = 1.0000, NPV = 0.9817, and F1-score for malignant = 0.98). QSVM achieved its highest accuracy at PCA = 4 (92.98%), AUC = 0.9622, Sensitivity = 0.9219, Precision = 0.8939, NPV = 0.9524, and F1-score for malignant = 0.91. Across the three PCA configurations, QSVM exhibited a non-monotonic relationship between PCA dimensions and quantum kernel performance . These findings provide a quantitative benchmark for comparing quantum and classical machine learning in breast cancer diagnosis.
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 one of the most prevalent and life-threatening diseases affecting women worldwide, and early detection plays a crucial role in improving survival rates and treatment outcomes.Conventional diagnostic methods such as mammography, ultrasound imaging, and histopathological analysis rely heavily on expert interpretation and classical machine learning techniques.Although classical deep learning models, especially Convolutional Neural Networks (CNNs), have demonstrated impressive performance in medical image analysis, they face inherent limitations related to computational complexity, scalability, and the curse of dimensionality when handling large, high-resolution datasets.With the rapid advancement of quantum computing, Quantum Machine Learning (QML) has emerged as a promising paradigm capable of addressing these challenges by leveraging quantum phenomena such as superposition and entanglement.This research explores the application of Quantum Convolutional Neural Networks (Quantum CNNs or QCNNs) for breast cancer detection, aiming to enhance classification accuracy while reducing computational overhead.The proposed approach integrates quantum circuits with classical preprocessing techniques to extract discriminative features from breast cancer datasets, such as mammographic or histopathological images.By encoding image features into quantum states and performing convolution operations through parameterized quantum circuits, the Quantum CNN model is designed to learn complex patterns more efficiently than classical counterparts.The study evaluates the effectiveness of Quantum CNNs in distinguishing between benign and malignant breast tumors and compares its performance with traditional CNN models.The findings highlight the potential of Quantum CNNs to revolutionize medical image analysis by offering faster convergence, improved generalization, and robustness against noise.This research contributes to the growing field of quantumassisted healthcare systems and demonstrates how quantum computing can be harnessed to address critical challenges in breast cancer diagnosis.