Quantum machine learning (QML) is considered one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in medicine and biology fields is still in its infancy with few examples. In this study, we aim to assess whether quantum kernels (QK) could effectively classify subtypes of breast cancer (BC) patients based on molecular characteristics. We performed a heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the quantum processing unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on NISQ devices. Our findings suggest that QK methods show promises for application in precision oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically.
Quantum machine learning (QML) is considered one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in medicine and biology fields is still in its infancy with few examples. In this study, we aim to assess whether quantum kernels (QK) could effectively classify subtypes of breast cancer (BC) patients based on molecular characteristics. We performed a heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the quantum processing unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on NISQ devices. Our findings suggest that QK methods show promises for application in precision oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically.
Subtyping of lung cancer is crucial for individualized treatment and optimizing outcomes. Computational modeling allows data analysis over large scales, which helps in making precision classification in large datasets or high-dimensional datasets. Conventional machine learning methods perform well, but integration of multi-omic data (DNA, RNA, miRNA) is challenging in cancer subtyping because of high dimensionality and complex inter-omic relationships. To bypass this Quantum machine learning (QML) provides better ability to navigate high-dimensional biological data with many variables using quantum computational concepts that can identify fine patterns or small-scale patterns beyond the capability of classical approaches. Through the integration of quantum computing principles with the machine learning framework, the study highlights the improvement of computer technology. The framework shows how the system is optimized with the help of quantum principles, how it can improve accuracy, parallel processing and reducing latency. In this work, we have combined data on miRNA, RNA expression, and DNA methylation to create a complete workflow for classifying cancer. This method uses a wide variety of feature selection techniques, such as random forest-based selection, chi-square, mutual information(MI), and principal component analysis(PCA).In order to illustrate the notion of quantum advantage, we carefully compare the performance metrics of the Quantum Support Vector Classifier (QSVC) to the classical Support Vector Machine (SVM), using a ZZ feature map quantum kernel as our baseline for analysis. Using five-fold crossvalidation, we have also verified that the quantum has an advantage with consistently lower variance and higher accuracy.
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.
Introduction: In recent years, high-dimensional clinical and genomic data have gained significant importance for prognosis and personalized medicine in breast cancer. But the use of quantum machine learning (QML) on such data is limited by the availability of few qubits, the computation time of quantum simulation, and dimensionality reduction. This work systematically compares several QML architectures for breast cancer classification in the presence of realistic and simulator constraints. Methods: The experiments were performed on a dataset of METABRIC breast cancer patients (2,509 patients). After handling missing values and one-hot encoding, there were 63 features in the processed data set. The feature space was reduced by Principal Component Analysis (PCA) to 12, 4 and 2 components for the implementations of quantum computers, respectively, with 56.10 ± 0.14%, 26.78 ± 0.57% and 16.23 ± 0.33% of the variance retained. Three QML models were tested: Quantum Neural Networks (QNN), Quantum K-Nearest Neighbors (QKNN), and Quantum Support Vector Machines (QSVM), with the models being simulated. Seven classical classification models were tested: Logistic Regression, SVM with RBF kernel, K-Nearest Neighbors, Random Forest, XGBoost, LightGBM and Multilayer Perceptron, both with PCA-matched and full 63-feature representation. All primary results are reported with 5-fold cross validation. Results: Among the evaluated QML architectures, QKNN using 12 principal components achieved the strongest performance, attaining an accuracy of **75.11% ± 3.76%**, an F1-score of **0.7088 ± 0.0433**, and a ROC-AUC of **0.8148 ± 0.0394**. Even though all of the QML models performed significantly poorly in comparison to classical models trained on the entire 63-feature data set, the latter models were able to achieve about **94% accuracy** with XGBoost, Random Forest, and Logistic Regression. The comparison also showed the effect of information loss due to PCA is significant in predictive performance in both classical and quantum models. Discussion: The results show that for high dimensional breast cancer data, currently available simulator-based QML models can learn meaningful patterns with limited quantum resources, but are not as effective as powerful classical machine learning models when complete feature representations are available. The study does not report any sort of quantum advantage or clinical use, but rather a benchmark of current QML architectures that has been rigorously calculated and repeated, with a focus on the impact of dimensionality reduction, validation approaches, and simulator limitations, as well as outlining challenges that need to be overcome prior to practical implementation on real quantum hardware.
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.
Early and accurate diagnosis of breast cancer plays a critical role in improving patient survival rates and reducing treatment costs. Classical machine learning algorithms such as Logistic Regression and Support Vector Machines (SVM) have demonstrated strong performance in medical diagnosis tasks; however, their scalability and representational limits motivate the exploration of emerging paradigms. Quantum Machine Learning (QML), particularly Variational Quantum Classifiers (VQC), has recently gained attention due to its potential to model complex decision boundaries using quantum circuits. This paper presents a comparative analysis between a Variational Quantum Classifier and classical machine learning models for binary breast cancer classification. The study employs dimensionality-reduced clinical data and evaluates model performance using accuracy, confusion matrices, decision boundary visualization, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) curves. Experimental results indicate that the VQC achieves competitive performance with classical approaches, demonstrating its feasibility for small-scale medical datasets and highlighting the promise of quantum-enhanced learning in healthcare applications.
Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182, MIR520a, PYGO2, EMX2OS, EMX2, RP11-550F7.1, RPS11, MIR20a, EIF4G2, IGLV1-47, RP11-180M15.7, SCYL3, AC107081.5, C2orf47, FST, MIR7-1, SLC22A18, CLDN5, and AC007879.7 identified as having significant and non-significant p-values asso-ciated with LUAD patient predictions.