  Quantum computing holds significant potential for applications in biology and
medicine, spanning from the simulation of biomolecules to machine learning
approaches for subtyping cancers on the basis of clinical features. This
potential is encapsulated by the concept of a quantum advantage, which is
typically contingent on a reduction in the consumption of a computational
resource, such as time, space, or data. Here, we distill the concept of a
quantum advantage into a simple framework that we hope will aid researchers in
biology and medicine pursuing the development of quantum applications. We then
apply this framework to a wide variety of computational problems relevant to
these domains in an effort to i) assess the potential of quantum advantages in
specific application areas and ii) identify gaps that may be addressed with
novel quantum approaches. Bearing in mind the rapid pace of change in the
fields of quantum computing and classical algorithms, we aim to provide an
extensive survey of applications in biology and medicine that may lead to
practical quantum advantages.
