Genetic Algorithms and Quantum Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Gilson A. Giraldi and Renato Portugal and 
                 Ricardo N. Thess",
  title =        "Genetic Algorithms and Quantum Computation",
  institution =  "National Laboratory for Scientific Computing,
                 Petropolis, RJ, Brazil",
  year =         "2004",
  number =       "0403003",
  keywords =     "genetic algorithms, genetic programming, Quantum
                 Computing, Evolutionary Strategies",
  URL =          "",
  URL =          "",
  abstract =     "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
                 emphasise 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.",
  size =         "27 pages",

Genetic Programming entries for Gilson A Giraldi Renato Portugal Ricardo N Thess