research-article

Performance analysis of derandomized evolution strategies in quantum control experiments

Authors:

Jonathan Roslund,

Herschel RabitzAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 519 - 526

https://doi.org/10.1145/1389095.1389193

Published: 12 July 2008 Publication History

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.

References

[1]

D. V. Arnold and H.--G. Beyer. Local Performance of the (μ/μ_I, łambda)-ES in a Noisy Environment. In W. Martin and W. Spears, editors, Foundations of Genetic Algorithms, 6, pages 127--141, San Francisco, CA, 2001. Morgan Kaufmann.

[2]

A. Auger and N. Hansen. Performance evaluation of an advanced local search evolutionary algorithm. In Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005, pages 1777--1784, Piscataway, NJ, USA, 2005. IEEE Press.

[3]

H.-G. Beyer and H.-P. Schwefel. Evolution strategies a comprehensive introduction. Natural Computing: an international journal, 1(1):3--52, 2002.

Digital Library

[4]

R. Bracewell. The Fourier Transform and Its Applications. McGraw-Hill Book Company, 1965.

[5]

R. Fanciulli, L. Willmes, J. Savolainen, P. van der Walle, T. Back, and J. L. Herek. Evolution strategies for laser pulse compression. In Proceedings of the International Conference Evolution Artificielle, volume 4926 of Lecture Notes in Computer Science. Springer, 2008.

Digital Library

[6]

D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading, MA, 1989.

Digital Library

[7]

N. Hansen and S. Kern. Evaluating the CMA Evolution Strategy on Multimodal Test Functions. In Parallel Problem Solving from Nature -- PPSN V, volume 1498 of Lecture Notes in Computer Science, pages 282--291, Amsterdam, 1998. Springer.

[8]

N. Hansen and A. Ostermeier. Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation, 9(2):159--195, 2001.

Digital Library

[9]

T.-S. Ho and H. Rabitz. Why do effective quantum controls appear easy to find? Journal of Photochemistry and Photobiology A: Chemistry, 180(3), Jun 2006.

[10]

R. S. Judson and H. Rabitz. Teaching lasers to control molecules. Phys. Rev. Lett., 68(10):1500--1503, Mar 1992.

[11]

P. Nuernberger, G. Vogt, T. Brixner, and G. Gerber. Femtosecond quantum control of molecular dynamics in the condensed phase. Phys Chem Chem Phys., 9(20):2470--2497, 2007.

[12]

A. Ostermeier, A. Gawelczyk, and N. Hansen. A derandomized approach to self adaptation of evolution strategies. Evolutionary Computation, 2(4):369--380, 1994.

Digital Library

[13]

A. Ostermeier, A. Gawelczyk, and N. Hansen. Step-size adaptation based on non-local use of selection information. In Parallel Problem Solving from Nature -- PPSN III, volume 866 of Lecture Notes in Computer Science, pages 189--198. Springer, 1994.

Digital Library

[14]

A. P. Peirce, M. A. Dahleh, and H. Rabitz. Optimal control of quantum-mechanical systems: Existence, numerical approximation, and applications. Phys. Rev. A, 37(12), Jun 1988.

[15]

H. Rabitz, R. de Vivie-Riedle, M. Mutzkus, and K. Kompa. Whither the Future of Controlling Quantum Phenomena? Science, 288:824--828, May 2000.

[16]

J. Roslund, M. Roth, and H. Rabitz. Laboratory observation of quantum control level sets. Phys. Rev. A, 74, 2006.

[17]

M. Roth. Optimal Dynamic Discrimination in the Laboratory. PhD thesis, Princeton University, 2007.

[18]

S. Shi and H. Rabitz. Quantum mechanical optimal control of physical observables in microsystems. Chem. Phys., 92(364), Jan 1990.

[19]

O. M. Shir and T. Back. The Second Harmonic Generation Case Study as a gateway for ES to Quantum Control Problems. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2007, pages 713--721, New York, NY, USA, 2007. ACM Press.

Digital Library

[20]

O. M. Shir, J. N. Kok, M. J. Vrakking, and T. Back. Gaining insight into laser pulse shaping by evolution strategies. In IWINAC, volume 4527 of Lecture Notes in Computer Science, pages 467--477. Springer, 2007.

Digital Library

[21]

O. M. Shir, C. Siedschlag, T. Back, and M. J. Vrakking. Evolutionary algorithms in the optimization of dynamic molecular alignment. In 2006 IEEE World Congress on Computational Intelligence, pages 9817--9824. IEEE Computational Intelligence Society, 2006.

[22]

W. S. Warren, H. A. Rabitz, and M. Dahleh. Coherent Control of Quantum Dynamics: The Dream Is Alive. Science, 259:1581--1589, Mar 1993.

[23]

T. C. Weinacht and P. H. Bucksbaum. Using feedback for coherent control of quantum systems. Journal of Optics B, 4(3), 2002.

[24]

D. Zeidler, S. Frey, K.-L. Kompa, and M. Motzkus. Evolutionary algorithms and their application to optimal control studies. Phys. Rev. A, 64(2):023420, Jul 2001.

Cited By

Shir OXing XRabitz H(2021)Multi-level evolution strategies for high-resolution black-box controlJournal of Heuristics10.1007/s10732-021-09483-zOnline publication date: 2-Aug-2021
https://doi.org/10.1007/s10732-021-09483-z
Shir ORoslund JLeghtas ZRabitz H(2012)Quantum control experiments as a testbed for evolutionary multi-objective algorithmsGenetic Programming and Evolvable Machines10.1007/s10710-012-9164-713:4(445-491)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1007/s10710-012-9164-7
Shir ORoslund JRabitz HRothlauf F(2009)Evolutionary multi-objective quantum control experiments with the covariance matrix adaptationProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1569992(659-666)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1569992

Index Terms

Performance analysis of derandomized evolution strategies in quantum control experiments
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Cited By

Shir OXing XRabitz H(2021)Multi-level evolution strategies for high-resolution black-box controlJournal of Heuristics10.1007/s10732-021-09483-zOnline publication date: 2-Aug-2021
https://doi.org/10.1007/s10732-021-09483-z
Shir ORoslund JLeghtas ZRabitz H(2012)Quantum control experiments as a testbed for evolutionary multi-objective algorithmsGenetic Programming and Evolvable Machines10.1007/s10710-012-9164-713:4(445-491)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1007/s10710-012-9164-7
Shir ORoslund JRabitz HRothlauf F(2009)Evolutionary multi-objective quantum control experiments with the covariance matrix adaptationProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1569992(659-666)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1569992

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