Transactions in Theoretical and Mathematical Physics

Transactions in Theoretical and Mathematical Physics

Adaptive numerical optimization for high-fidelity quantum gate vontrol in atom Interferometers

Document Type : Original Article

Author
Electrical and Computer Engineering Department, Qom University of Technology
Abstract
The atomic interferometer has two quantum unitary gates that must be realized for quantum sensing purposes: the atomic gravimeter and the atomic interferometer gyroscope. An optimal cost function that defines the distance between two unitary operators is defined. Based on it, general adaptive (GADA) algorithms for optimization-based quantum control are innovated to realize the atomic mirror and beam-splitter gates. We used optimal quantum control to realize those atom interferometer gates. We obtained gate fidelities of 0.99980 and 0.99998 for the mirror and the beam-splitter gates, respectively. In this research, a two-level atom system with clock transitions1S_0 -->3P_0 of strontium (87Sr) atom was employed.
Keywords
Subjects

1. C.-W. Chou, D. B. Hume, T. Rosenband, D. J.
Wineland, Science 329, (2010)#

2. X. Wuet al., Sci. Adv. 5, (2019)#

3. T. Kovachy et al., Nature 528, (2015)#

4. A. Loeb, D. Maoz, arXiv:1501.00996, (2015)#

5. M. Abe et al., Matter-wave Atomic Gradiometer Inter
ferometric Sensor, IOP Publishing (2021)#

6. A. Arvanitaki, J. Huang, K. Van Tilburg, Phys. Rev. D,
91 (2015)#

7. M.-S. Zhan et al., Int. J. Mod. Phys. D, 29, (2020)#

8. T. Kovachy et al., Macroscopic scale atom interferome
ters: introduction, techniques, and applications, Oxford
University Press (2019)#

9. J. Fang, J. Qin, Sensors, 12, (2012)#

10. T. L. Gustavson, Ph.D. thesis, Stanford University
(2000)#

11. J. Zhang, D. Burgarth, R. Laflamme, D. Suter, Phys.
Rev. A 91, (2015)#

12. S. Kwon, A. Tomonaga, G. L. Bhai, S. J. Devitt, J.-S.
Tsai, J. Appl. Phys., 129, (2021)#

13. P. Krantz et al. Appl. Phys. Rev., 6, (2019)#

14. H.Häffner, C. F. Roos, R. Blatt, Phys. Rep., 469, (2008)#

15. D. S. Weiss, M. Saffman, Phys. Today, 70, (2017)#

16. L. Henriet et al. Quantum, 4, (2020)#

17. G. M. Huang, T. J. Tarn, J. W. Clark, J. Math. Phys. 24,
(1983)#

18. V. P. Belavkin, Autom. Remote Control, (1983)#

19. W. S. Warren, H. Rabitz, M. Dahleh, Science, 259,
(1993)#

20. S. Chu, Nature, 416, (2002)#

21. H. Robbins, S. Monro, Ann. Math. Stat., (1951)#

22. H. B. McMahan, M. Streeter, Proc. 23rd Annu. Conf.
Learn. Theory (COLT), (2010)#

23. D. Zhou et al. Trans. Mach. Learn. Res. (2024)#

24. N. Qian, Neural networks, 12, (1999)#

25. D. P. Kingma, J. Ba, Proc. 3rd Int. Conf. Learn. Repre
sent. (ICLR) (2015)#

26. Nelles, Oliver, Nonlinear system identification, IOP
Publishing (2002)#

27. M. D. Zeiler, arXiv:1212.5701, (2012)#

28. Berman, Paul R, Atom interferometry, Academic press,
(1997)#

29. I. Glendinning, ResearchGate presentation, (2010)#
 
Volume 2, Issue 2
Spring 2025
Pages 84-90

  • Receive Date 07 April 2025
  • Revise Date 22 May 2025
  • Accept Date 29 May 2025
  • First Publish Date 29 May 2025
  • Publish Date 01 May 2025