Performance Analysis for Genetic Quantum Circuit Synthesis

  • Cristian Ruican
  • Mihai Udrescu
  • Lucian Prodan
  • Mircea Vladutiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)


Genetic algorithms have proven their ability in detecting optimal or closed-to-optimal solutions to hard combinational problems. However, determining which crossover, mutation or selector operator is best for a specific problem can be cumbersome. The possibilities for enhancing genetic operators are discussed herein, starting with an analysis of their run-time performance. The contribution of this paper consist of analyzing the performance gain from the dynamic adjustment of the genetic operators, with respect to overall performance, as applied for the task of quantum circuit synthesis. We provide experimental results demonstrating the effectiveness of the approach by comparing our results against a traditional GA, using statistical significance measurements.


Genetic Algorithm Adaptive Algorithm Genetic Operator Quantum Circuit Dynamic Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cristian Ruican
    • 1
  • Mihai Udrescu
    • 1
  • Lucian Prodan
    • 1
  • Mircea Vladutiu
    • 1
  1. 1.Advanced Computing Systems and Architectures LaboratoryUniversity ”Politehnica” TimisoaraTimisoaraRomania

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