Breast cancer continues to pose a formidable global health challenge, claiming countless lives each year and highlighting the urgent need for early and precise detection. Timely diagnosis is critical for improving treatment outcomes and survival rates, yet conventional screening and diagnostic methods often fall short in handling complex medical data efficiently. Over the past two decades, artificial intelligence (AI) and machine learning (ML) have revolutionized breast cancer detection by analyzing medical imaging, histopathology, and genomic profiles. Despite their success, these classical approaches are constrained by high computational costs, reliance on large annotated datasets, and reduced generalization when data is limited or heterogeneous. Quantum Machine Learning (QML) has emerged as a transformative paradigm, integrating quantum computing principles—such as superposition, entanglement, and quantum parallelism—with classical learning frameworks to overcome these limitations. This survey provides a comprehensive overview of QML applications in breast cancer detection by reviewing key methodologies and advancements. It begins with a discussion of the clinical profile, treatment approaches, and recent epidemiological trends of breast cancer. Subsequently, it examines traditional AI and ML techniques used in diagnostic modeling, followed by emerging QML frameworks that leverage quantum principles for enhanced learning and efficiency. The paper concludes by outlining current challenges in QML research, including hardware constraints, noise sensitivity, and the lack of standardized evaluation benchmarks.
Breast cancer continues to pose a formidable global health challenge, claiming countless lives each year and highlighting the urgent need for early and precise detection. Timely diagnosis is critical for improving treatment outcomes and survival rates, yet conventional screening and diagnostic methods often fall short in handling complex medical data efficiently. Over the past two decades, artificial intelligence (AI) and machine learning (ML) have revolutionized breast cancer detection by analyzing medical imaging, histopathology, and genomic profiles. Despite their success, these classical approaches are constrained by high computational costs, reliance on large annotated datasets, and reduced generalization when data is limited or heterogeneous. Quantum Machine Learning (QML) has emerged as a transformative paradigm, integrating quantum computing principles—such as superposition, entanglement, and quantum parallelism—with classical learning frameworks to overcome these limitations. This survey provides a comprehensive overview of QML applications in breast cancer detection by reviewing key methodologies and advancements. It begins with a discussion of the clinical profile, treatment approaches, and recent epidemiological trends of breast cancer. Subsequently, it examines traditional AI and ML techniques used in diagnostic modeling, followed by emerging QML frameworks that leverage quantum principles for enhanced learning and efficiency. The paper concludes by outlining current challenges in QML research, including hardware constraints, noise sensitivity, and the lack of standardized evaluation benchmarks.
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.
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.
Breast cancer is the most diagnosed cancer among women worldwide. As early detection significantly improves survival rates, accurate and efficient imaging diagnostics play a critical role in clinical decision-making. Traditional machine learning (ML) methods have demonstrated promising capabilities in medical imaging tasks. However, they often require large datasets and substantial computational resources, which limit their adaptability in resource-constrained environments. Quantum-Inspired Machine Learning (QI-ML) has emerged as a promising paradigm that leverages principles of quantum mechanics, such as superposition and entanglement, within classical computing architectures. This approach aims to address challenges such as model complexity, memory limitations, and generalization to small datasets. In this review, we explore the application of QI-ML to breast cancer imaging across modalities, including mammography, ultrasound, and Magnetic Resonance Imaging, discussing key algorithmic strategies, advantages over classical ML, and existing gaps.
Cancer remains a major global health challenge, necessitating the development of accurate, scalable, and clinically reliable diagnostic approaches. Artificial intelligence (AI) has significantly enhanced cancer detection through advanced analysis of medical imaging, histopathology, and multi-omics data. However, conventional AI models face limitations related to computational scalability, optimization complexity, and data heterogeneity. Quantum computing introduces a fundamentally different computational paradigm based on superposition, entanglement, and probabilistic state evolution. The integration of quantum computing with machine learning referred to as Quantum Artificial Intelligence (QAI) has emerged as a promising approach to address these challenges. This review critically evaluates the role of QAI in cancer detection through a systematic analysis of literature published between 2015 and 2026. The findings suggest that QAI can enhance feature representation, accelerate optimization processes, and improve predictive performance. Hybrid quantum-classical models show potential in tumor classification, multi-omics integration, and real-time clinical decision-making. Despite these advances, significant challenges remain, including hardware limitations, lack of large-scale validation, and ethical concerns. Overall, QAI represents a promising yet emerging paradigm with potential to transform early cancer detection and precision oncology. This review uniquely synthesizes quantum-AI approaches in oncology, emphasizing hybrid quantum-classical models as the most practical translational pathway for clinical adoption.
Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.
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.