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A variance reduction technique for the stochastic Liouville–von Neumann equation

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Abstract

The stochastic Liouville–von Neumann equation provides an exact numerical simulation strategy for quantum systems interacting with Gaussian reservoirs [J.T. Stockburger, H. Grabert, PRL 88, 170407 (2002)]. Its scaling with the extension of the time interval covered has recently improved dramatically through time-domain projection techniques [J.T. Stockburger, EPL 115, 40010 (2016)]. Here, we present a sampling strategy which results in a significantly improved scaling with the strength of the dissipative interaction, based on reducing the non-unitary terms in sample propagation through convex optimization techniques.

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References

  1. R. Alicki, K. Lendi, in Quantum Dynamical Semigroups and Applications, Lecture Notes in Physics (Springer, Berlin, 1987), Vol. 286

  2. E.B. Davies, Commun. Math. Phys. 39, 91 (1974)

    Article  ADS  Google Scholar 

  3. H.-P. Breuer, F. Petruccione, The Theory of Open Quantum Systems (Oxford University Press, Oxford, 2002), p. 625

  4. A. Levy, R. Kosloff, EPL (Europhys. Lett.) 107, 20004 (2014)

    Article  ADS  Google Scholar 

  5. J.T. Stockburger, T. Motz, Fortschr. Phys. 65, 1600067 (2017)

    Article  Google Scholar 

  6. R. Alicki, D.A. Lidar, P. Zanardi, Phys. Rev. A 73, 052311 (2006)

    Article  ADS  MathSciNet  Google Scholar 

  7. R. Schmidt et al., Phys. Rev. Lett. 107, 130404 (2011)

    Article  ADS  Google Scholar 

  8. R.P. Feynman, F.L. Vernon, Ann. Phys. (N.Y.) 24, 118 (1963)

    Article  ADS  Google Scholar 

  9. U. Weiss, in Quantum Dissipative Systems, Series in Modern Condensed Matter Physics, 3rd edn. (World Scientific, Singapore, 2008), Vol. 13

  10. R. Egger, L. Mühlbacher, C.H. Mak, Phys. Rev. E 61, 5961 (2000)

    Article  ADS  Google Scholar 

  11. L. Mühlbacher, J. Ankerhold, C. Escher, J. Chem. Phys. 121, 12696 (2004)

    Article  ADS  Google Scholar 

  12. Y. Tanimura, P.G. Wolynes, Phys. Rev. A 43, 4131 (1991)

    Article  ADS  Google Scholar 

  13. Y. Tanimura, J. Chem. Phys. 141, 044114 (2014)

    Article  ADS  Google Scholar 

  14. J.T. Stockburger, H. Grabert, Phys. Rev. Lett. 88, 170407 (2002)

    Article  ADS  Google Scholar 

  15. J. Cao, L.W. Ungar, G.A. Voth, J. Chem. Phys. 104, 4189 (1996)

    Article  ADS  Google Scholar 

  16. W.T. Strunz, Phys. Lett. A 224, 25 (1996)

    Article  ADS  MathSciNet  Google Scholar 

  17. J. Shao, J. Chem. Phys. 120, 5053 (2004)

    Article  ADS  Google Scholar 

  18. Y. Tanimura, J. Phys. Soc. Jpn. 75, 082001 (2006)

    Article  Google Scholar 

  19. J.T. Stockburger, EPL (Europhys. Lett.) 115, 40010 (2016)

    Article  ADS  Google Scholar 

  20. R. Kubo, J. Phys. Soc. Jpn. 17, 1100 (1962)

    Article  Google Scholar 

  21. C.W. Gardiner, in Stochastic Methods: A Handbook for the Natural and Social Sciences, Springer Series in Synergetics, 4th edn. (Springer, Berlin, 2009), Vol. 13

  22. H. Imai, Y. Ohtsuki, H. Kono, Chem. Phys. 446, 134 (2015)

    Article  Google Scholar 

  23. J.T. Stockburger, Chem. Phys. 296, 159 (2004)

    Article  Google Scholar 

  24. W. Koch, F. Großmann, J.T. Stockburger, J. Ankerhold, Phys. Rev. Lett. 100, 230402 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  25. M. Grant, S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, version 2.1 (2014), https://doi.org/cvxr.com/cvx (accessed on December 16, 2017)

  26. M. Grant, S. Boyd, in Recent Advances in Learning and Control, Lecture Notes in Control and Information Sciences, edited by V. Blondel, S. Boyd, H. Kimura (Springer, Berlin, 2008), pp. 95–110

  27. A.J. Leggett et al., Rev. Mod. Phys. 59, 1 (1987)

    Article  ADS  Google Scholar 

  28. A.J. Leggett et al., Rev. Mod. Phys. 67, 725 (1995) (erratum)

    Article  ADS  Google Scholar 

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Correspondence to Jürgen T. Stockburger.

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Schmitz, K., Stockburger, J.T. A variance reduction technique for the stochastic Liouville–von Neumann equation. Eur. Phys. J. Spec. Top. 227, 1929–1937 (2019). https://doi.org/10.1140/epjst/e2018-800094-y

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  • DOI: https://doi.org/10.1140/epjst/e2018-800094-y

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