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Principal Control Analysis: Gaining Insight from Feedback Learning Algorithms

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Ultrafast Phenomena XIV

Part of the book series: Springer Series in Chemical Physics ((CHEMICAL,volume 79))

Abstract

Feedback learning algorithms are widely used to search for optical pulse shapes for quantum control. We propose a simple analysis of the pulse shapes to learn about the degrees of freedom in the effective control Hamiltonian. When applied to stimulated Raman scattering in methanol, the technique yields results consistent with a simple model of two-mode SRS in methanol.

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References

  1. D. Tannor and S. Rice, Adv. Chem. Phys. 70, 441 (1988).

    Article  Google Scholar 

  2. P. Brumer and M Shapiro, Ann. Rev. Phys. Chem. 43, 257 (1992).

    Article  ADS  Google Scholar 

  3. D. Tannor, R. Kosloff, and S. Rice, J. Chem. Phys. 85, 5805 (1986).

    Article  ADS  Google Scholar 

  4. A. Peirce, M. Dahleh, and H. Rabitz, Phys. Rev. A 37, 4950 (1988).

    Article  MathSciNet  ADS  Google Scholar 

  5. A. Shnitman, et al., Phys. Rev. Lett. 76, 2886 (1996).

    Article  ADS  Google Scholar 

  6. L. Zhu, et al., Science 270, 77 (1995).

    Article  ADS  Google Scholar 

  7. R. Judson and H. Rabitz, Phys. Rev. Letters 68, 1500 (1992).

    Article  ADS  Google Scholar 

  8. J. Holland, Scientific American 267, 66 (1992).

    Article  Google Scholar 

  9. L. Davis, ed., Handbook of Genetic Algorithms (Van Norstrand Reinhold, New York, 1991).

    Google Scholar 

  10. J. Geremia, E. Weiss, and H. Rabitz, Chemical Physics 267, 209 (2001).

    Article  ADS  Google Scholar 

  11. A. Mitra and H. Rabitz, Physical Review A 67, 33407 (2003).

    Article  ADS  Google Scholar 

  12. C. Daniel, et al., Science 299, 536 (2003).

    Article  ADS  Google Scholar 

  13. J. Geremia and H. Rabitz, Phys. Rev. Lett. 89, 263902 (2002).

    Article  ADS  Google Scholar 

  14. I. Jolliffe, Principal Component Analysis (Springer Verlag, 2002), 2nd ed.

    Google Scholar 

  15. J. L. White, B. J. Pearson, and P. H. Bucksbaum, lanl.arXiv.org (2004), quant-ph/0401018.

    Google Scholar 

  16. B. J. Pearson, et al., Phys. Rev. A 63, 063412/1 (2001).

    Article  ADS  Google Scholar 

  17. B. J. Pearson and P.H. Bucksbaum, Phys. Rev. Lett. 92, 243003 (2004).

    Article  ADS  Google Scholar 

  18. D. Meshulach and Y. Silberberg, Nature 396, 239 (1998).

    Article  ADS  Google Scholar 

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White, J.L., Pearson, B.J., Bucksbaum, P.H. (2005). Principal Control Analysis: Gaining Insight from Feedback Learning Algorithms. In: Kobayashi, T., Okada, T., Kobayashi, T., Nelson, K.A., De Silvestri, S. (eds) Ultrafast Phenomena XIV. Springer Series in Chemical Physics, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27213-5_32

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