TY - GEN
T1 - Performance analysis of derandomized evolution strategies in quantum control experiments
AU - Shir, Ofer M.
AU - Back, Thomas
AU - Roslund, Jonathan
AU - Rabitz, Herschel
PY - 2008/12/15
Y1 - 2008/12/15
N2 - 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.
AB - 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.
KW - CMA-ES
KW - Derandomized evolution strategies
KW - Experimental quantum control
KW - Laser pulse shaping
UR - http://www.scopus.com/inward/record.url?scp=57349155620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57349155620&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:57349155620
SN - 9781605581309
T3 - GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
SP - 519
EP - 526
BT - GECCO'08
T2 - 10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
Y2 - 12 July 2008 through 16 July 2008
ER -