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.
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.
Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Quantum Machine Learning (QML) is an emerging, powerful paradigm at the intersection of quantum computing and artificial intelligence, with the potential to enhance medical image analysis. We compared their performance with classical counterparts, while critically examining the gap between theoretical quantum advantages and the practical constraints of current Noisy Intermediate-Scale Quantum (NISQ) implementations. This review explored the current landscape of QML-based methodologies applied to key medical imaging modalities - Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-rays, and histopathology. We systematically analyzed how quantum-enhanced models were employed for critical tasks such as image classification, segmentation, and reconstruction, and we compared their performance with that of classical counterparts. The review delved into the architectural innovations that enabled these quantum approaches, including hybrid quantum-classical frameworks, variational quantum circuits, and quantum convolutional neural networks. Particular emphasis was given to the clinical relevance of QML applications, from early diagnostics to real-time surgery planning, and how quantum advantage might influence data efficiency, interpretability, along computational scalability. Finally, we highlighted current challenges in data encoding, noise resilience, and hardware limitations, and discussed promising future directions that could enable the practical deployment of QML in healthcare settings. This comprehensive review aimed to serve as a foundational reference for researchers and practitioners looking to harness quantum computing in medical imaging and precision medicine.
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 transformative paradigm that leverages quantum mechanical phenomena superposition, entanglement, and quantum interference to enhance computational approaches in healthcare. This comprehensive survey systematically examines advances in QML for healthcare applications from 2020 to 2025, analyzing cutting-edge algorithms, recent implementations, and emerging prospects across drug discovery, medical imaging, genomics, and clinical decision support. We review Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and hybrid quantum-classical frameworks. Our analysis of 150+ recent publications reveals significant theoretical progress alongside persistent hardware constraints and algorithmic challenges. We provide a comprehensive taxonomy of QML algorithms, a comparative performance analysis against classical baselines, a detailed examination of NISQ-era limitations, and a technology roadmap spanning near-term hybrid approaches through long-term fault-tolerant quantum computing. This survey serves as an updated reference for researchers and practitioners navigating the rapidly evolving intersection of quantum computing and medical informatics.
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.
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making.