Skip to main content

Advertisement

Springer Nature Link
Log in

Preparation of three-atom GHZ states based on deep reinforcement learning

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

Generally, stimulated Raman adiabatic passage technology has been used to generate the Greenberger–Horne–Zeilinger state. Due to decoherence caused by long operation time, it is almost impossible to implement experimentally. To reduce the operation time, we propose a scheme to construct the shortcut to adiabatic passage based on deep reinforcement learning (DRL). Moreover, in order to facilitate the implementation, we have performed Gaussian fitting on the pulse sequence. Numerical analysis shows that our scheme has better performance than the Gradient Ascent Pulse Engineering and the Genetic Algorithm, and is robust to the leakage of the optical cavity as well as the spontaneous emission of atoms. Besides, we apply the DRL algorithm to another model and give the pulse sequence for the preparation of the three-atom singlet state with high fidelity and robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Born, M., Fock, V.: Beweis des Adiabatensatzes. Z. Phys. 51, 165–180 (1928)

    Article  ADS  Google Scholar 

  2. Vitanov, N.V., Rangelov, A.A., Shore, B.W., Bergmann, K.: Stimulated Raman adiabatic passage in physics, chemistry, and beyond. Rev. Mod. Phys. 89, 015006 (2017)

    Article  ADS  Google Scholar 

  3. Kang, Y.-H., Chen, Y.-H., Shi, Z.-C., Song, J., Xia, Y.: Fast preparation of W states with superconducting quantum interference devices by using dressed states. Phys. Rev. A 94, 052311 (2016)

    Article  ADS  Google Scholar 

  4. Chen, X., et al.: Fast optimal frictionless atom cooling in harmonic traps: Shortcut to adiabaticity. Phys. Rev. Lett. 104, 063002 (2010)

    Article  ADS  Google Scholar 

  5. Chen, Y.-H., Xia, Y., Chen, Q.-Q., Song, J.: Efficient shortcuts to adiabatic passage for fast population transfer in multiparticle systems. Phys. Rev. A 89, 033856 (2014)

    Article  ADS  Google Scholar 

  6. Lu, M., Xia, Y., Shen, L.-T., Song, J., An, N.B.: Shortcuts to adiabatic passage for population transfer and maximum entanglement creation between two atoms in a cavity. Phys. Rev. A 89, 012326 (2014)

    Article  ADS  Google Scholar 

  7. Chen, Y.-H., Xia, Y., Chen, Q.-Q., Song, J.: Fast and noise-resistant implementation of quantum phase gates and creation of quantum entangled states. Phys. Rev. A 91, 012325 (2015)

    Article  ADS  Google Scholar 

  8. Liang, Y., Wu, Q.-C., Su, S.-L., Ji, X., Zhang, S.: Shortcuts to adiabatic passage for multiqubit controlled-phase gate. Phys. Rev. A 91, 032304 (2015)

    Article  ADS  Google Scholar 

  9. Liang, Y., Ji, X., Wang, H.-F., Zhang, S.: Deterministic SWAP gate using shortcuts to adiabatic passage. Laser. Phys. Lett. 12, 115201 (2015)

    Article  ADS  Google Scholar 

  10. Wang, Z., Xia, Y., Chen, Y.-H., Song, J.: Fast controlled preparation of two-atom maximally entangled state and N-atom W state in the direct coupled cavity systems via shortcuts to adiabatic passage. Eur. Phys. J. D 70, 162 (2016)

    Article  ADS  Google Scholar 

  11. Chen, X., Torrontegui, E., Muga, J.G.: Lewis-Riesenfeld invariants and transitionless quantum driving. Phys. Rev. A 83, 062116 (2011)

    Article  ADS  Google Scholar 

  12. Khaneja, N., Reiss, T., Kehlet, C., Schulte-Herbrüggen, T., Glaser, S.J.: Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms. J. Magn. Reson. 172, 296–305 (2005)

    Article  ADS  Google Scholar 

  13. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433 (1950)

    Article  MathSciNet  Google Scholar 

  14. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)

    Article  ADS  Google Scholar 

  15. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press, Cambridge (2018)

    MATH  Google Scholar 

  16. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)

    MATH  Google Scholar 

  17. Porotti, R., Tamascelli, D., Restelli, M., Prati, E.: Coherent transport of quantum states by deep reinforcement learning. Commun. Phys. 2, 61 (2019)

    Article  Google Scholar 

  18. Porotti, R., Tamascelli, D., Restelli, M., Prati, E.: Reinforcement learning based control of coherent transport by adiabatic passage of spin qubits. J. Phys. Conf. Ser. 1275, 012019 (2019)

    Article  Google Scholar 

  19. Paparelle, I., Moro, L., Prati, E.: Digitally stimulated Raman passage by deep reinforcement learning. Phys. Lett. A 384, 126266 (2020)

    Article  Google Scholar 

  20. Yu, C.-S., Yi, X.-X., Song, H.-S., Mei, D.: Robust preparation of Greenberger–Horne–Zeilinger and W states of three distant atoms. Phys. Rev. A 75, 044301 (2007)

    Article  ADS  Google Scholar 

  21. Shao, X., Wu, J., Yi, X., Long, G.-L.: Dissipative preparation of steady Greenberger–Horne–Zeilinger states for Rydberg atoms with quantum Zeno dynamics. Phys. Rev. A 96, 062315 (2017)

    Article  ADS  Google Scholar 

  22. Wang, Y.-D., Chesi, S., Loss, D., Bruder, C.: One-step multiqubit Greenberger–Horne–Zeilinger state generation in a circuit QED system. Phys. Rev. B 81, 104524 (2010)

    Article  ADS  Google Scholar 

  23. Lin, J.-Z.: Robust preparation of atomic concatenated Greenberger–Horne–Zeilinger states via shortcuts to adiabaticity. Ann. Phys. 530, 1700456 (2018)

    Article  Google Scholar 

  24. Chen, Y.-H., Xia, Y., Song, J., Chen, Q.-Q.: Shortcuts to adiabatic passage for fast generation of Greenberger–Horne–Zeilinger states by transitionless quantum driving. Sci. Rep. 5, 15616 (2015)

    Article  ADS  Google Scholar 

  25. Huang, B.-H., Chen, Y.-H., Wu, Q.-C., Song, J., Xia, Y.: Fast generating Greenberger–Horne–Zeilinger state via iterative interaction pictures. Laser. Phys. Lett. 13, 105202 (2016)

    Article  ADS  Google Scholar 

  26. Cabello, A.: N-particle N-level singlet states: Some properties and applications. Phys. Rev. Lett. 89, 100402 (2002)

    Article  ADS  MathSciNet  Google Scholar 

  27. Yang, R.-C., et al.: Generation of singlet states with Rydberg blockade mechanism and driven by adiabatic passage. Quantum Inf. Process. 15, 731 (2016)

    Article  ADS  MathSciNet  Google Scholar 

  28. Brockman, G. et al.: OpenAI gym (2016). arXiv:1606.01540

  29. Johansson, J.R., Nation, P.D., Nori, F.: QuTiP: An open-source Python framework for the dynamics of open quantum systems. Comput. Phys. Commun. 183, 1760–1772 (2012)

    Article  ADS  Google Scholar 

  30. Johansson, J.R., Nation, P.D., Nori, F.: QuTiP 2: A Python framework for the dynamics of open quantum systems. Comput. Phys. Commun. 184, 1234–1240 (2013)

    Article  ADS  Google Scholar 

  31. Schulman, J., Levine, S., Moritz, P., Jordan, M. I. Abbeel, P.: Trust region policy optimization (2015). arXiv:1502.05477

  32. Hill, A. et al.: Stable baselines. https://github.com/hill-a/stable-baselines (2018)

  33. Spillane, S.M., et al.: Ultrahigh-Q toroidal microresonators for cavity quantum electrodynamics. Phys. Rev. A 71, 013817 (2005)

    Article  ADS  Google Scholar 

  34. van Rossum, G. Drake, F. L.: Python 3 Reference Manual CreateSpace, (2009)

  35. Abadi, M. et al.: TensorFlow: Large-scale machine learning on heterogeneous systems. https://www.tensorflow.org (2015)

  36. Chen, Z., Chen, Y.-H., Xia, Y., Song, J., Huang, B.-H.: Fast generation of three-atom singlet state by transitionless quantum driving. Sci. Rep. 6, 22202 (2016)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

The work was supported by the Fundamental Research Funds for the Central Universities under Grant No. 2020ZDPYMS03.

Author information

Authors and Affiliations

  1. School of Materials Science and Physics, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China

    Guang Hao Xue & Liang Qiu

Authors
  1. Guang Hao Xue
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Liang Qiu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guang Hao Xue.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, G.H., Qiu, L. Preparation of three-atom GHZ states based on deep reinforcement learning. Quantum Inf Process 20, 243 (2021). https://doi.org/10.1007/s11128-021-03172-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-021-03172-3

Keywords