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Nonadiabatic Quantum Search Algorithm with Analytical Success Rate

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Abstract

In nonadiabatic quantum search algorithm, it is difficult to calculate the success rate analytically. We develop the nonadiabatic quantum search algorithm by adding a counterdiabatic driving term to the original time-dependent Hamiltonian. The Hamiltonian we structured is diagonal in eigen picture and the time-independent Schrödinger equation is solved analytically. Then, we get an accurate analytical expression of success rate in nonadiabatic quantum search algorithm. Utilizing this expression, a sufficient condition, which can ensure the success rate be one with arbitrary evolution time, was found. Moreover, we can choose the better parameters by calculating the precise success rate according to the expression.

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References

  1. Grover, L.K.: Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett. 79(2), 325 (1997)

    Article  ADS  Google Scholar 

  2. Boyer, M., Brassard, G., Høyer, P., Tapp, A.: Tight bounds on quantum searching. Fortschr. Phys. 46, 493 (1998)

    Article  Google Scholar 

  3. Farhi, E., Goldstone, J., Gutmann, S., Siper, M.: Quantum Computation by Adiabatic Evolution. arXiv:quant-ph/001106 (2000)

  4. Born, M., Fock, V.: Beweis des Adiabatensatzes. Z. Phys. 51, 165 (1928)

    Article  ADS  MATH  Google Scholar 

  5. Mizel, A., Lidar, D.A., Mitchell, M.: Simple proof of equivalence between adiabatic quantum computation and the circuit model. Phys. Rev. Lett. 99(07), 070502 (2007)

    Article  ADS  Google Scholar 

  6. Roland, J., Cerf, N.J.: Quantum search by local adiabatic evolution. Phys. Rev. A 65(04), 042308 (2002)

    Article  ADS  Google Scholar 

  7. Sun, J., Lu, S.F., Liu, F.: Partial adiabatic quantum search algorithm and its extensions. Quantum Inf. Process. 12(8), 2689 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  8. Oh, S., Kais, S.: Transitionless driving on adiabatic search algorithm. J. Chem. Phys. 141(22), 224108 (2014)

    Article  ADS  Google Scholar 

  9. Hu, H.Y., Wu, B.: Optimizing quantum adiabatic algorithm. Phys. Rev. A 93(01), 012345 (2016)

    Article  ADS  Google Scholar 

  10. Pérez, A., Romanelli, A.: Nonadiabatic quantum search agorithms. Phys. Rev. A 76(05), 052318 (2007)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  11. Berry, M.V.: Transitionless quantum driving. J. Phys. A 42(36), 365303 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. Chen, X., Lizuain, I., Ruschhaupt, A., Guéry-Odelin, D., Muga, J.G.: Shortcut to adiabatic passage in two- and three-level atoms. Phys. Rev. Lett. 105(12), 123003 (2010)

    Article  ADS  Google Scholar 

  13. del Campo, A.: Shortcuts to adiabaticity by counterdiabatic driving. Phys. Rev. Lett. 111(10), 100502 (2013)

    Article  ADS  Google Scholar 

  14. Torrontegui, E., Ibáñez, S., Martínez-Garaot, S., Modugno, M., Campo, A., Gué-Odelin, D., Ruschhaupt, A., Chen, X., Muga, J.G.: Shortcuts to adiabaticity. Adv. At., Mol. Opt. Phys. 62, 117 (2013)

    Article  ADS  Google Scholar 

  15. Chen, Y.H., Xia, Y., Wu, Q.C., Huang, B.H., Song J.: Method for constructing shortcuts to adiabaticity by a substitute of counterdiabatic driving terms. Phys. Rev. A 93(05), 052109 (2016)

    Article  ADS  Google Scholar 

  16. Grover, L.K.: Fixed-point quantum search. Phys. Rev. Lett. 95(15), 150501 (2005)

    Article  ADS  MATH  Google Scholar 

  17. Das, S., Kobes, R., Kunstatter, G.: Energy and efficiency of adiabatic quantum search algorithms. J. Phys. A 36, 2839 (2003)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  18. Roland, J., Cerf, N.J.: Quantum-circuit model of Hamiltonian search algorithms. Phys. Rev. A 68(06), 062311 (2003)

    Article  ADS  Google Scholar 

  19. van Dam, W., Mosca, M., Vazirani, U.: How powerful is adiabatic quantum computation? In: Proc. 42nd IEEE Symposium on Foundations of Computer Science, pp. 279–287 (2001)

  20. Cleve, R., Gottesman, D., Mosca, M., Somma, R.D., Yonge-Mallo, D.L.: Efficient discrete-time simulations of continuous-time quantum query algorithms. In: Proc. 41st ACM Symposium on Theory of Computing, pp. 409–416 (2009)

  21. Hen, I.: Quantum gates with controlled adiabatic evolutions. Phys. Rev. A 91(02), 022309 (2015)

    Article  ADS  Google Scholar 

  22. Santos, A.C., Sarandy, M.S.: Superadiabatic controlled evolutions and universal quantum computation. Sci. Rep. 5, 15775 (2015)

    Article  ADS  Google Scholar 

  23. Coulamy, I.B., Santos, A.C., Hen, I., Sarandy, S.: Energetic cost of superadiabatic quantum computation. Front. ICT 3, 19 (2016)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Natural Science Foundation of China (NSFC) under Grant No.11504430.

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Correspondence to Wan-Su Bao.

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Li, FG., Bao, WS., Li, T. et al. Nonadiabatic Quantum Search Algorithm with Analytical Success Rate. Int J Theor Phys 58, 939–949 (2019). https://doi.org/10.1007/s10773-018-3986-x

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  • DOI: https://doi.org/10.1007/s10773-018-3986-x

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