Volume 14, Issue 1 (Journal of Control, V.14, N.1 Spring 2020)                   JoC 2020, 14(1): 65-71 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Arjmandzadeh A, Yarahmadi M. Designing a Quantum Genetic Controller for Tracking the Path of Quantum Systems. JoC 2020; 14 (1) :65-71
URL: http://joc.kntu.ac.ir/article-1-430-en.html
Abstract:   (5761 Views)
Based on learning control methods and computational intelligence, control of quantum systems is an attractive field of study in control engineering. What is important is to establish control approach ensuring that the control process converges to achieve a given control objective and at the same time it is simple and clear. In this paper, a learning control method based on genetic quantum controller approach is presented. For tracking a time variant function trajectory, in a closed quantum system, the presented controller is used. For this purpose a constrained optimization problem, based on minimization of difference between a given trajectory and system states subject to an iteration relation of the dynamical solution be satisfied, is designed. According to high convergence rate in quantum genetic algorithm, a quantum genetic algorithm for solving the optimization problem is used. A stochastic measure for observation the initial population is used. Efficient an optimal tracking, with at least tracking errors and at least learned chattering are advantages of the presented method. A couple of examples for demonstrating the advantages are simulated. Simulation results reflect the good performance of the proposed method for controlling the quantum systems.
Full-Text [PDF 628 kb]   (1592 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2016/12/15 | Accepted: 2018/09/4 | Published: 2020/06/11

1. [1] A. Daeichian and F. Sheikholeslam, 2012, "Survey and Comparison of Quantum Systems: Modeling, Stability and Controllability", Journal of Control, Vol. 5, No. 4, pp. 20-31.
2. [2] C. Chen, L. C. Wang and Y. Wang, 2013, "Closed-Loop and Robust Control of Quantum Systems", The Scientific World Journal, Vol. 2013, 869285-1-869285-11. [DOI:10.1155/2013/869285]
3. [3] A. Narayanan and M. Moore, 1996, "Quantum-inspired genetic algorithms", in Proceedings of the IEEE International Conference on Evolutionary Computation, pp.61-66, Nagoya, Japan.
4. [4] J. Liu, S. Cong, and Y. Zhu, 2012, "Adaptive Trajectory Tracking of Quantum Systems", 12th International Conference on Control, Automation and Systems, Jeju Island, Korea, Oct. 17-21.
5. [5] W. Zhu and H. Rabitz, 2003, "Quantum control design via adaptive tracking", Journal of Chemical Physics, Vol. 119, No 7, pp. 3619-3625. [DOI:10.1063/1.1582847]
6. [6] Z. Sahebi and M. Yarahmadi, 2018, "Switching optimal adaptive trajectory tracking control of quantum systems", Optim Control Appl Meth. DOI: 10.1002/oca.2412. [DOI:10.1002/oca.2412]
7. [7] D. Dong and I. R. Petersen, 2011, "Quantum control theory and applications: A survey", IET Control Theory & Applications, Vol. 4, No. 12, pp. 2651-2671. [DOI:10.1049/iet-cta.2009.0508]
8. [8] H. Sedghee Rostami and B. Rezaie, 2016, "Controlling state of quantum system using fuzzy controller", Modares Mechanical Engineering, Vol. 16, No. 9, pp. 124-134, (in Persian)
9. [9] Z. Sahebi and M. Yarahmadi, 2018, "Hybrid adaptive intelligent controller design using quantum wavelet neural networks for trajectory tracking control in finite dimensional closed quantum systems", Modares Mechanical Engineering, Vol. 18, No. 02, pp. 179-188. (in Persian)
10. [10] A. Arjmandzadeh and M. Yarahmadi, 2017, "Quantum genetic learning control of quantum ensembles with Hamiltonian uncertainties", Entropy, Vol. 19, No. 8, pp. 1-12. [DOI:10.3390/e19080376]
11. [11] D. D'Alessandro, Introduction to Quantum Control and Dynamics, Taylor & Francis Group, LLC, 2008.
12. [12] M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, New York, 2010. [DOI:10.1017/CBO9780511976667]
13. [13] H. Wang, J. Liu, J. Zhi, and C. Fu, 2013, "The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization", Hindawi Publishing Corporation Mathematical Problems in Engineering. [DOI:10.1155/2013/730749]
14. [14] Z. Laboudi and S. Chikhi, 2011, "Comparison of Genetic Algorithm and Quantum Genetic Algorithm", The International Arab Journal of Information Technology, Vol. 9, No. 3, pp. 243-249.
15. [15] D. Dong, M. A. Mabrok, I. R. Petersen, B. Qi, C. Chen and H. Rabitz, 2015, "Sampling-Based Learning Control for Quantum Systems With Uncertainties", IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol. 23, NO. 6, 2155-2166. [DOI:10.1109/TCST.2015.2404292]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2023 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb