The state of quantum computing applications in health and medicine
Frederik F. Fl\"other

TL;DR
This review maps the recent landscape of quantum computing applications in health and medicine, highlighting experimental and theoretical studies across genomics, diagnostics, and treatments, with a focus on quantum machine learning advancements.
Contribution
It provides a comprehensive overview of over 40 studies on quantum computing in medicine, emphasizing recent progress and future challenges in clinical applications.
Findings
Quantum machine learning shows competitive performance with classical methods.
QML algorithms are trained on diverse clinical datasets for various medical tasks.
The review discusses technical and ethical challenges in applying quantum computing to medicine.
Abstract
Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theory have arguably been entangled ever since Schr\"odinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be…
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Taxonomy
TopicsGenetics, Bioinformatics, and Biomedical Research
