  The pace and unpredictability of evolution are critically relevant in a
variety of modern challenges: combating drug resistance in pathogens and
cancer, understanding how species respond to environmental perturbations like
climate change, and developing artificial selection approaches for agriculture.
Great progress has been made in quantitative modeling of evolution using
fitness landscapes, allowing a degree of prediction for future evolutionary
histories. Yet fine-grained control of the speed and the distributions of these
trajectories remains elusive. We propose an approach to achieve this using
ideas originally developed in a completely different context: counterdiabatic
driving to control the behavior of quantum states for applications like quantum
computing and manipulating ultra-cold atoms. Implementing these ideas for the
first time in a biological context, we show how a set of external control
parameters (i.e. varying drug concentrations / types, temperature, nutrients)
can guide the probability distribution of genotypes in a population along a
specified path and time interval. This level of control, allowing empirical
optimization of evolutionary speed and trajectories, has myriad potential
applications, from enhancing adaptive therapies for diseases, to the
development of thermotolerant crops in preparation for climate change, to
accelerating bioengineering methods built on evolutionary models, like directed
evolution of biomolecules.
