Quantum Computing, Quantum Machine Learning, and Quantum Mechanical Principles in Medical Physics: A PRISMA 2020-Compliant Systematic Review

Abstract

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.