Quantum Computing as a Foundational Framework for Advancing Cancer Research

Jiaran Li, Li Li, Ning Liu +3 more

Rajesh G, Madhumitha R, Sabarinath Arunagiri +1 more

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.

Siddhi Ramesh, Teague Tomesh, Samantha J. Riesenfeld +2 more

Mandeep Kaur Saggi, Sabre Kais

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.

Maria Shahzadi, Muhammad Bilal

Soumith Mudam, K K Girish, DR. BISWAJIT BHOWMIK

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.