Quantum Machine Learning (QML) is an emerging field with significant potential to transform oncology. This systematic literature review evaluates the current landscape of QML applications in cancer research, analyzing 21 primary research articles, selected from 410 initially identified, published between 2020 and 2024. The review categorizes QML applications, assesses evidence for quantum advantage, identifies technical challenges, and outlines future directions. Findings indicate robust research activity, particularly in China and India, with hybrid quantum-classical algorithms showing promise in simulated environments for tasks like diagnosis and prediction, especially with small or imbalanced datasets. However, translation into clinical practice remains constrained by hardware limitations and the lack of rigorous real-world validation, resulting in a persistent gap between simulation and clinical application. In addition, most studies adopt quantitative experimental approaches, with limited exploration of qualitative perspectives such as usability, interpretability, and workflow integration. To address these gaps, future research should pursue standardized frameworks, mixed-method evaluation protocols, and cross-disciplinary collaboration between quantum scientists, clinicians, and implementation researchers. Such efforts would provide robust, comparable evidence of clinical utility, ensure trustworthiness in deployment, and accelerate the transition of QML from experimental promise to transformative impact in oncology.
Quantum Machine Learning (QML) is an emerging field with significant potential to transform oncology. This systematic literature review evaluates the current landscape of QML applications in cancer research, analyzing 21 primary research articles, selected from 410 initially identified, published between 2020 and 2024. The review categorizes QML applications, assesses evidence for quantum advantage, identifies technical challenges, and outlines future directions. Findings indicate robust research activity, particularly in China and India, with hybrid quantum-classical algorithms showing promise in simulated environments for tasks like diagnosis and prediction, especially with small or imbalanced datasets. However, translation into clinical practice remains constrained by hardware limitations and the lack of rigorous real-world validation, resulting in a persistent gap between simulation and clinical application. In addition, most studies adopt quantitative experimental approaches, with limited exploration of qualitative perspectives such as usability, interpretability, and workflow integration. To address these gaps, future research should pursue standardized frameworks, mixed-method evaluation protocols, and cross-disciplinary collaboration between quantum scientists, clinicians, and implementation researchers. Such efforts would provide robust, comparable evidence of clinical utility, ensure trustworthiness in deployment, and accelerate the transition of QML from experimental promise to transformative impact in oncology.
Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary paradigm that integrates quantum computing and machine learning to address limitations of classical approaches in processing complex high-dimensional heterogeneous and imbalanced medical data. This study presents a systematic literature review of QML applications in healthcare based on 94 primary studies published between 2020 and April 2025. The review follows Kitchenham’s guidelines and the PRISMA framework to ensure rigor transparency and reproducibility. Seven research questions were formulated to examine publication trends sources datasets topics quantum model categories algorithms methods and evaluation metrics. Findings reveal a sharp increase in publication activity after 2023 indicating growing academic interest in healthcare-oriented QML. Medical imaging datasets were most frequently used followed by signal-based and tabular datasets with 86.5% of studies relying on public data. Hybrid classical-quantum approaches including CNN-QSVM Quantum Neural Networks and Variational Quantum Classifiers were dominant because they combine classical feature extraction with quantum-enhanced learning. However most studies were conducted in simulation environments rather than real hardware reflecting NISQ limitations including noise decoherence limited qubits and circuit depth constraints. Many results used small curated or imbalanced datasets raising concerns regarding generalizability reproducibility and clinical validity. Although numerous studies reported high accuracy standardized evaluation protocols external validation and uncertainty assessment remain limited. Overall QML in healthcare remains exploratory with substantial potential but significant barriers. This review highlights progress gaps and future directions emphasizing clinically representative datasets robust benchmarking interpretable noise-resilient models and real-hardware implementation to support trustworthy deployment in healthcare systems worldwide settings today.
This research project presents a PRISMA-guided systematic literature review exploring the application of quantum machine learning (QML) in oncology, a rapidly growing field with fragmented evidence. The study synthesizes peer-reviewed studies from six databases (ACM, IEEE, MDPI, Scopus, Wiley, and PubMed) published between 2020 and 2026, screening 212 records and selecting 49 studies for analysis. The review examines clinical tasks, cancer domains, QML architectures, challenges, and future directions, revealing that current research mainly focuses on classification and detection, particularly in breast cancer and brain tumors, using hybrid quantum-classical models. Despite promising results, progress is limited by hardware constraints, simulator dependence, and insufficient clinical validation. The findings provide a clear overview of current QML trends and highlight future priorities, including real quantum hardware testing, diverse clinical datasets, standardized benchmarking, and stronger collaboration between computing and oncology communities.
Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research.
Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.
Quantum machine learning (QML) leverages quantum phenomena such as superposition, entanglement, and interference to address computational challenges that are difficult for classical systems. In radiation oncology, where treatment planning, image guidance, adaptive replanning, and multi-modal data integration require high-dimensional optimisation and complex pattern recognition, QML may offer advantages in computational efficiency and representational capacity. This review systematically maps quantum algorithms to the clinical radiation oncology workflow. We outline foundational concepts in quantum computing, including complexity insights from the BQP class and practical constraints of the noisy intermediate-scale quantum (NISQ) era. The radiation oncology pipeline-from consultation and simulation to treatment planning, delivery, and follow-up-is analysed to identify computational bottlenecks where quantum methods could provide benefit. Current developments in quantum-enabled diagnostic modelling, quantum-inspired clinical decision support for precision radiotherapy, quantum reinforcement learning for adaptive treatment policies, and emerging concepts such as quantum digital twins are reviewed. Key challenges-including hardware limitations, barren plateaus, data encoding constraints, limited datasets, regulatory considerations, and the gap between theoretical speedups and clinical implementation-are critically examined. Finally, a three-horizon roadmap is proposed outlining near-term proof-of-concept research, mid-term integration with treatment planning systems, and long-term prospects for fault-tolerant quantum simulation in oncology.