  The Schrodinger equation is one of the most important equations in physics
and chemistry and can be solved in the simplest cases by computer numerical
methods. Since the beginning of the 70s of the last century the computer began
to be used to solve this equation in elementary quantum systems, e.g. and in
the most complex case a hydrogen-like system. Obtaining the solution means
finding the wave function, which allows predicting the physical and chemical
properties of the quantum system. However, when a quantum system is more
complex than a hydrogen-like system then we must be satisfied with an
approximate solution of the equation. During the last decade the application of
algorithms and principles of quantum computation in disciplines other than
physics and chemistry, such as biology and artificial intelligence, has led to
the search for alternative techniques with which to obtain approximate
solutions of the Schrodinger equation. In this paper, we review and illustrate
the application of genetic algorithms, i.e. stochastic optimization procedures
inspired by Darwinian evolution, in elementary quantum systems and in quantum
models of artificial intelligence. In this last field, we illustrate with two
toy models how to solve the Schrodinger equation in an elementary model of a
quantum neuron and in the synthesis of quantum circuits controlling the
behavior of a Braitenberg vehicle.
