Applied Physics B

, 122:112 | Cite as

Optimizing quantum gas production by an evolutionary algorithm

  • T. Lausch
  • M. Hohmann
  • F. Kindermann
  • D. Mayer
  • F. Schmidt
  • A. Widera


We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a \(^{87}\)rubidium Bose–Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.



The project was financially supported partially by the European Union via the ERC Starting Grant 278208 and partially by the DFG via SFB/TR49. D. M. is a recipient of a DFG fellowship through the Excellence Initiative by the Graduate School Materials Science in Mainz (GSC 266), F. S. acknowledges funding by Studienstiftung des deutschen Volkes, and T. L. acknowledges funding from Carl-Zeiss Stiftung.


  1. 1.
    R. Desbuquois et al., Nat. Phys. 8, 645 (2012)CrossRefGoogle Scholar
  2. 2.
    M.W. Zwierlein, J.R. Abo-Shaeer, A. Schirotzek, C.H. Schunck, W. Ketterle, Nature 435, 1047 (2005)ADSCrossRefGoogle Scholar
  3. 3.
    W. Ketterle, D.S. Durfee, D.M. Stamper-Kurn, in Proceedings of International School of Physics Enrico Fermi (1999), p. 67Google Scholar
  4. 4.
    Z.W. Barber et al., Phys. Rev. Lett. 100, 103002 (2008)ADSCrossRefGoogle Scholar
  5. 5.
    P. Rosenbusch et al., Phys. Rev. A 79, 13404 (2009)ADSCrossRefGoogle Scholar
  6. 6.
    D.L. Stern, Nat. Rev. Genet. 14, 751 (2013)CrossRefGoogle Scholar
  7. 7.
    T. Baumert, T. Brixner, V. Seyfried, M. Strehle, G. Gerber, Appl. Phys. B Lasers Opt. 65, 779 (1997)ADSCrossRefGoogle Scholar
  8. 8.
    B.J. Pearson, J.L. White, T.C. Weinacht, P.H. Bucksbaum, Phys. Rev. A 63, 063412 (2001)ADSCrossRefGoogle Scholar
  9. 9.
    M. Tsubouchi, T. Momose, Phys. Rev. A 77, 052326 (2008)ADSCrossRefGoogle Scholar
  10. 10.
    J. Roslund, H. Rabitz, Phys. Rev. A 79, 53417 (2009)ADSCrossRefGoogle Scholar
  11. 11.
    D. Picard, A. Revel, M. Cord, in 2008 International Workshop on Content-Based Multimedia Indexing, CBMI 2008, Conference Proceedings, vol. 10 (2008), p. 439Google Scholar
  12. 12.
    J. Kennedy, R. Eberhart, Proceedings of IEEE International Conference on Neural Networks, 1995, vol. 4 (1995), p. 1942Google Scholar
  13. 13.
    L.J. Fogel, Intelligence Through Simulated Evolution, 1st edn. (Wiley-Interscience, London, 1966)MATHGoogle Scholar
  14. 14.
    G.S. Hornby, J.D. Lohn, D.S. Linden, Evol. Comput. 19, 1 (2011)CrossRefGoogle Scholar
  15. 15.
    K. Price, R. Storn, J.A. Lampinen, Differential Evolution, Natural Computing Series, 1st edn. (Springer, Berlin, 2005)MATHGoogle Scholar
  16. 16.
    W. Rohringer et al., Appl. Phys. Lett. 93, 264101 (2008)ADSCrossRefGoogle Scholar
  17. 17.
    W. Rohringer, D. Fischer, M. Trupke, T. Schumm, J. Schmiedmayer, in Stochastic Optimization-Seeing the Optimal for the Uncertain, ed. by I. Dritsas (InTech, Rijeka, 2011), pp. 3–28. doi:10.5772/15480
  18. 18.
    I. Geisel et al., Appl. Phys. 102, 214105 (2013)ADSGoogle Scholar
  19. 19.
    P.B. Wigley, et al., arXiv:1507.04964, 1 (2015)
  20. 20.
    J. Zhang, A.C. Sanderson, Adaptive Differential Evolution, vol. 1 of Evolutionary Learning and Optimization (Springer, Berlin, 2009)Google Scholar
  21. 21.
    S. Das, A. Konar, U. Chakraborty, 2005 IEEE Congress in Evolutionary Computation, vol. 2 (IEEE, 2005), pp. 1691–1698Google Scholar
  22. 22.
    A.M. Steane, C.J. Foot, Europhys. Lett. 14, 231 (1991)ADSCrossRefGoogle Scholar
  23. 23.
    H.J. Metcalf, P. van der Straten, Laser Cooling and Trapping, 16th edn. (Springer, New York, 1999)CrossRefGoogle Scholar
  24. 24.
    R. Grimm, M. Weidemüller, Y.B. Ovchinnikov, Adv. At. Mol. Opt. Phys. 42, 95 (2000)ADSCrossRefGoogle Scholar
  25. 25.
    M. Hohmann et al., EPJ Quantum Technol. 2, 23 (2015)CrossRefGoogle Scholar
  26. 26.
    A. Handl, Multivariate Analysemethoden, 2nd edn. (Springer, Berlin, 2010)CrossRefMATHGoogle Scholar
  27. 27.
    W. Ketterle, K. Davis, M. Joffe, A. Martin, D. Pritchard, Phys. Rev. Lett. 70, 2253 (1993)ADSCrossRefGoogle Scholar
  28. 28.
    H.J. Lewandowski, D.M. Harber, D.L. Whitaker, E.A. Cornell, J. Low Temp. Phys. 132, 309 (2003)ADSCrossRefGoogle Scholar
  29. 29.
    J.F. Clément et al., Phys. Rev. A 79, 061406(R) (2009)ADSCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • T. Lausch
    • 1
  • M. Hohmann
    • 1
  • F. Kindermann
    • 1
  • D. Mayer
    • 1
    • 2
  • F. Schmidt
    • 1
    • 2
  • A. Widera
    • 1
    • 2
  1. 1.Department of Physics and Research Center OPTIMASKaiserslauternGermany
  2. 2.Graduate School Materials Science in MainzKaiserslauternGermany

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