Objectives: The convergence of quantum computing, quantum machine learning, and quantum mechanical principles represents an emerging paradigm in medical physics. Classical methods encounter hard limits in treatment planning, molecular-scale drug simulation, and medical image interpretation. The four quantum phenomena superposition, entanglement, quantum tunneling, and quantum coherence offer distinct routes around these barriers through both hardware sensing and algorithmic frameworks.To systematically identify, appraise, and synthesise evidence on applications of quantum computing, quantum machine learning, and quantum mechanical principles in medical physics, mapping the evidence base, assessing clinical readiness, and proposing a research roadmap.Materials and Methods: A PRISMA 2020-compliant systematic search was conducted across PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, arXiv, and bioRxiv (January 2015 – March 2026). Two independent reviewers screened 5,248 identified records. After deduplication, screening, and eligibility assessment, 133 studies were included in the qualitative synthesis and 89 in the quantitative performance comparison. Methodological quality was assessed using a modified CLAIM checklist and QUADAS-2 framework.Results: Four application clusters emerged. Superposition underpins hyperpolarized ¹³C MRI (technology readiness level (TRL) 7) and hybrid quantum CNN architectures. Entanglement enables OPM-MEG neuroimaging (TRL 6–7) and quantum kernel classification. Quantum tunneling drives radiotherapy optimisation (TRL 4–5). Quantum coherence supports variational algorithms for drug discovery (TRL 2–3) and quantum dot theranostics (TRL 3–4). Hybrid quantum–classical methods outperformed classical baselines, though most studies showed high risk of bias.Conclusions: Quantum-enhanced sensing and hybrid quantum–classical treatment planning are credible near-term clinical translation targets. Fault-tolerant drug simulation and large-scale quantum AI await hardware maturation beyond 2030. Critical enablers include standardised benchmarking, quantum-specific regulatory guidance, and equity planning.
Objectives: The convergence of quantum computing, quantum machine learning, and quantum mechanical principles represents an emerging paradigm in medical physics. Classical methods encounter hard limits in treatment planning, molecular-scale drug simulation, and medical image interpretation. The four quantum phenomena superposition, entanglement, quantum tunneling, and quantum coherence offer distinct routes around these barriers through both hardware sensing and algorithmic frameworks.To systematically identify, appraise, and synthesise evidence on applications of quantum computing, quantum machine learning, and quantum mechanical principles in medical physics, mapping the evidence base, assessing clinical readiness, and proposing a research roadmap.Materials and Methods: A PRISMA 2020-compliant systematic search was conducted across PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, arXiv, and bioRxiv (January 2015 – March 2026). Two independent reviewers screened 5,248 identified records. After deduplication, screening, and eligibility assessment, 133 studies were included in the qualitative synthesis and 89 in the quantitative performance comparison. Methodological quality was assessed using a modified CLAIM checklist and QUADAS-2 framework.Results: Four application clusters emerged. Superposition underpins hyperpolarized ¹³C MRI (technology readiness level (TRL) 7) and hybrid quantum CNN architectures. Entanglement enables OPM-MEG neuroimaging (TRL 6–7) and quantum kernel classification. Quantum tunneling drives radiotherapy optimisation (TRL 4–5). Quantum coherence supports variational algorithms for drug discovery (TRL 2–3) and quantum dot theranostics (TRL 3–4). Hybrid quantum–classical methods outperformed classical baselines, though most studies showed high risk of bias.Conclusions: Quantum-enhanced sensing and hybrid quantum–classical treatment planning are credible near-term clinical translation targets. Fault-tolerant drug simulation and large-scale quantum AI await hardware maturation beyond 2030. Critical enablers include standardised benchmarking, quantum-specific regulatory guidance, and equity planning.
Abstract Quantum technologies — encompassing quantum sensing, quantum computing and machine learning (QML), quantum nanomaterials, and quantum cryptography — represent an emerging class of physical and computational tools with the potential to address fundamental limitations in clinical medicine across diagnostics, therapeutics, drug discovery, and health-information security. Despite a rapidly growing body of peer-reviewed evidence, estimated at a compound annual growth rate of approximately 14.8% over 2016–2025, the extant literature organises findings by technology type rather than clinical specialty, limiting accessibility for clinically-oriented readers and precluding systematic cross-domain technology maturity comparison. This review addresses that structural gap through a specialty-first synthesis of the quantum-medicine literature and the introduction of an original five-dimension Translational Readiness Taxonomy (TRT: Hardware Maturity, Validation Setting, Benchmarking Rigour, Reproducibility, Regulatory Pathway) as a replicable cross-domain maturity assessment instrument. A total of 168 peer-reviewed studies were identified through a reproducible seven-step PRISMA-aligned screening pipeline from a deduplicated corpus of 14,303 records (Scopus n = 7,081; Web of Science n = 4,644; PubMed n = 2,578), organised across five clinical domains: cardiology , neurology , oncology, genomics and drug discovery , and health data security ; 2022; Indumathi et al. 2024; Roosan et al. 2025; Freyer et al. 2025; Roosan et al. 2024; Lo et al. 2024; Singh et al. . Three original synthesising frameworks are additionally introduced: the Quantum-Medicine Translation Pipeline (QMTP), the Evidence-Maturity Bubble Matrix, and the Quantum-Medicine Ecosystem Map. Neurology leads translational readiness across hardware maturity, clinical validation, and benchmarking dimensions, driven by progressive optically-pumped-magnetometer magnetoencephalography (OPM-MEG) clinical adoption. Oncology, despite constituting 41.7% of included studies and generating the four highest-cited records, exhibits the lowest TRT composite score, with no prospective patient-recruited trials identified across its 70 included studies. A QML-framing-versus-evidence gap is confirmed in cardiology and oncology, where near-term clinical QML claims lack the computational proof-of-concept infrastructure that would logically need to precede them. Health data security leads regulatory maturity via the 2024 NIST finalisation of post-quantum cryptographic standards (CRYSTALS-Kyber/FIPS 203)
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.
Background and Objectives: Quantum machine learning (QML) leverages quantum computing principles to enhance artificial intelligence applications in health care. We aimed to conduct a systematic literature review following Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines to evaluate QML and quantum-assisted artificial intelligence (AI) applications in orthopedics and medicine.Materials and Methods: Comprehensive searches were performed across SciSpace, Google Scholar, and PubMed, yielding 1,068 initial records. After deduplication (819 unique records), abstract screening (threshold ≥4.0), and full-text screening (threshold ≥4.5), 97 studies met the inclusion criteria. Screening criteria included medical and orthopedic application, quantum-enhanced AI intervention, comparative performance evaluation, and clinical/computational outcome reporting. Data extraction focused on study design, quantum ML methods, medical application domains, key findings, performance comparisons, and clinical implications. Prediction model Risk of Bias Assessment Tool (PROBAST) guidelines were used to assess risk of the studies that report superior performance of QML over conventional ML or others.Results: The 97 included studies demonstrated diverse applications of QML across multiple medical domains, including orthopedics, oncology, cardiology, neurology, ophthalmology, and other specialties. Quantum approaches included quantum neural networks, quantum support vector machines, variational quantum classifiers, hybrid quantum-classical models, quantum transfer learning, and quantum federated learning. Performance comparisons consistently showed quantum-enhanced methods achieving comparable or superior accuracy to classical approaches. The overall risk of bias for all 11 studies with superior performance was determined as high risk using PROBAST guidelines.Conclusions: QML demonstrates significant promise for enhancing diagnostic accuracy, treatment optimization, and predictive analytics in orthopedics and medicine. Although current implementations face challenges related to hardware limitations, scalability, and clinical validation requirements, the evidence suggests that hybrid quantum-classical approaches offer practical pathways toward clinical adoption with the awareness of reports with high risk of bias. Future studies should focus on large-scale clinical trials and real-world validation of QML.
Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).
Background. Speech and audio understanding underpin affective computing, human–computer interaction, and clinical decision support. Classical deep-learning systems for tasks such as speech emotion recognition (SER) are increasingly bound by data demand, parameter scale, and cross-corpus brittleness. Quantum machine learning (QML) offers a complementary paradigm for high-dimensional, temporally structured signals but has not been systematically synthesised. Methods. We conducted a PRISMA-aligned systematic review of QML methods applied to speech and audio understanding. Six databases (IEEE Xplore, ACM, Scopus, Web of Science, PubMed, arXiv) were searched for studies published between January 2019 and April 2026. Of 2,623 deduplicated records, 55 were confirmed as primary (Tier-1) evidence, with 272 retained as adjacent or backbone evidence. Each Tier-1 study was scored against an eight-question quality instrument and entered into a comprehensive comparison matrix. Results. The corpus splits into 16 SER studies, 24 other speech tasks, and 15 general audio and music studies. Variational and parameterised quantum circuits (18 studies), quantum kernel methods (9), and quantum convolutional networks (8) dominate. Three structural weaknesses recur: aggressive classical preprocessing precedes quantum encoding in nearly every study; 26/55 score zero on NISQ-noise consideration; only 2 perform a formal significance test for the central quantum-versus-classical comparison. Conclusion. The field is in early but rapid and uneven growth (with annual outputs ranging from 2 to 16 papers across 2019–2025) but not yet ready to displace classical foundation models. We synthesise a method-by-task taxonomy and three-horizon research roadmap, and operationalise an eight-item benchmarking protocol as a community call to address recurrent reliability gaps and help raise the floor of QML–audio research.
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.