  Recently, researchers have applied genetic algorithms (GAs) to address some
problems in quantum computation. Also, there has been some works in the
designing of genetic algorithms based on quantum theoretical concepts and
techniques. The so called Quantum Evolutionary Programming has two major
sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic
Algorithms (QGAs). The former adopts qubit chromosomes as representations and
employs quantum gates for the search of the best solution. The later tries to
solve a key question in this field: what GAs will look like as an
implementation on quantum hardware? As we shall see, there is not a complete
answer for this question. An important point for QGAs is to build a quantum
algorithm that takes advantage of both the GA and quantum computing parallelism
as well as true randomness provided by quantum computers. In the first part of
this paper we present a survey of the main works in GAs plus quantum computing
including also our works in this area. Henceforth, we review some basic
concepts in quantum computation and GAs and emphasize their inherent
parallelism. Next, we review the application of GAs for learning quantum
operators and circuit design. Then, quantum evolutionary programming is
considered. Finally, we present our current research in this field and some
perspectives.
