Clinical applications of quantum mechanics

Ji‐Yong Sung, Jae‐Ho Cheong

BACKGROUND: Key biological processes underlying health and disease-including electron transfer, redox regulation, and radical-mediated signaling-are fundamentally governed by quantum-mechanical principles. These processes are central to mitochondrial function, metabolism, and cellular signaling, yet their biomedical implications have remained difficult to address using classical computational approaches. RATIONALE: Recent advances in quantum computing, quantum sensing, and quantum machine learning enable direct simulation and measurement of quantum phenomena in biologically relevant systems. Hybrid quantum-classical algorithms, such as the Variational Quantum Eigensolver and Quantum Phase Estimation, now provide first-principles access to redox potentials, electronic couplings, and spin-dependent reactions that are directly linked to disease mechanisms. These developments establish the foundation for quantum biomedicine as a translational framework bridging molecular physics and clinical medicine. CONTENT: This review synthesizes current progress in the application of quantum technologies to biomedicine, emphasizing translational relevance. We discuss quantum-informed modeling of cancer metabolism and redox rewiring, protein misfolding in neurodegenerative diseases, immune and inflammatory signaling, infectious disease mechanisms, and drug discovery. We further propose a Quantum-Experimental-Clinical (QEC) pipeline that integrates quantum simulations with experimental validation and multi-omics clinical data, enabling mechanistic interpretation of disease phenotypes and identification of redox- and spin-sensitive therapeutic targets. CONCLUSION: Quantum biomedicine introduces a new mechanistic layer that links electronic-scale processes to clinical phenotypes. While current implementations are constrained by NISQ-era hardware, rapid advances in quantum algorithms and sensing technologies position quantum approaches as emerging tools in precision and translational medicine. Strategic integration of quantum methods with experimental and clinical workflows may accelerate biomarker discovery and therapeutic development. KEY POINTS: Quantum biomedicine redefines life as a dynamic equilibrium sustained by quantum coherence, tunnelling and redox resonance. Hybrid quantum-classical algorithms, such as VQE and QPE, enable first-principles modelling of redox and spin-dependent reactions with near-experimental accuracy. NISQ-era hardware supports proof-of-concept simulations of electron tunnelling and radical-pair dynamics, bridging computation with measurable biophysics. Integration of quantum simulations with spectroscopy and cryo-EM establishes a quantum-experimental-clinical (QEC) pipeline linking theory, experiment and medicine. Ethical, educational and governance frameworks are essential for equitable, transparent and sustainable implementation of quantum health technologies.

Alexander V. Chalyi

Chandani Arora, Ramandeep Sandhu, Sardar M. N. Islam

The healthcare industry is undergoing a fundamental revolution with the integration of Quantum Natural Language Processing techniques for matching patient-clinical trials. This research envisions a paradigm where patients and clinical trials collaborate, changing the way healthcare is supplied and medical research is conducted. Apart from this, the research focuses on engaging patients in clinical trials. Patient involvement refers to the process by which patients and their healthcare providers collaborate to make decisions regarding their health. Within the framework of clinical studies, this may entail participants providing input on study designs or monitoring their data through wearable technology and smartphone apps. Quantum NLP has the potential to enhance patient outcomes and advance medical knowledge more quickly by enabling more precise and effective clinical trials. By setting the framework for future applications of quantum NLP, this study expands the conversation on the connection between quantum computing and healthcare.

Ingemar T. Ernberg, Göran Johansson, Tomas Lindblad +2 more

Yanche Ari Kustiawan, Khairil Imran Ghauth

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.