  A common goal in the sciences is optimization of an objective function by
selecting control variables such that a desired outcome is achieved. This
scenario can be expressed in terms of a control landscape of an objective
considered as a function of the control variables. At the most basic level, it
is known that the vast majority of quantum control landscapes possess no traps,
whose presence would hinder reaching the objective. This paper reviews and
extends the quantum control landscape assessment, presenting evidence that the
same highly favorable landscape features exist in many other domains of
science. The implications of this broader evidence are discussed. Specifically,
control landscape examples from quantum mechanics, chemistry, and evolutionary
biology are presented. Despite the obvious differences, commonalities between
these areas are highlighted within a unified mathematical framework. This
mathematical framework is driven by the wide ranging experimental evidence on
the ease of finding optimal controls (in terms of the required algorithmic
search effort beyond laboratory set up overhead). The full scope and
implications of this observed common control behavior pose an open question for
assessment in further work.
