Performance analysis of derandomized evolution strategies in quantum control experiments

Ofer M. Shir, Thomas Back, Jonathan Roslund, Herschel Rabitz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Genetic Algorithms (GAs) are historically the most commonly used optimization method in Quantum Control (QC) experiments. We transfer specific Derandomized Evolution Strategies (DES) that have performed well on noise-free theoretical Quantum Control calculations, including the Covariance Matrix Adaptation (CMA-ES) algorithm, into the noisy environment of Quantum Control experiments. We study the performance of these DES variants in laboratory experiments, and reveal the underlying strategy dynamics of first- versus second-order landscape information. It is experimentally observed that global maxima of the given QC landscapes are located when only first-order information is used during the search. We report on the disruptive effects to which DES are exposed in these experiments, and study covariance matrix learning in noisy versus noise-free environments. Finally, we examine the characteristic behavior of the algorithms on the given landscapes, and draw some conclusions regarding the use of DES in QC laboratory experiments.

Original languageEnglish (US)
Title of host publicationGECCO'08
Subtitle of host publicationProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
Pages519-526
Number of pages8
StatePublished - Dec 15 2008
Event10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States
Duration: Jul 12 2008Jul 16 2008

Publication series

NameGECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Other

Other10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
CountryUnited States
CityAtlanta, GA
Period7/12/087/16/08

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Keywords

  • CMA-ES
  • Derandomized evolution strategies
  • Experimental quantum control
  • Laser pulse shaping

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  • Cite this

    Shir, O. M., Back, T., Roslund, J., & Rabitz, H. (2008). Performance analysis of derandomized evolution strategies in quantum control experiments. In GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp. 519-526). (GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008).