Quantum Information Processing

, Volume 13, Issue 6, pp 1353–1379 | Cite as

SQR: a simple quantum representation of infrared images

  • Suzhen Yuan
  • Xia  Mao
  • Yuli Xue
  • Lijiang Chen
  • Qingxu Xiong
  • Angelo Compare


A simple quantum representation (SQR) of infrared images is proposed based on the characteristic that infrared images reflect infrared radiation energy of objects. The proposed SQR model is inspired from the Qubit Lattice representation for color images. Instead of the angle parameter of a qubit to store a color as in Qubit Lattice representation, probability of projection measurement is used to store the radiation energy value of each pixel for the first time in this model. Since the relationship between radiation energy values and probability values can be quantified for the limited radiation energy values, it makes the proposed model more clear. In the process of image preparation, only simple quantum gates are used, and the performance comparison with the latest flexible representation of quantum images reveals that SQR can achieve a quadratic speedup in quantum image preparation. Meanwhile, quantum infrared image operations can be performed conveniently based on SQR, including both the global operations and local operations. This paper provides a basic way to express infrared images in quantum computer.


Infrared image Quantum computation Image preparation  Qubit lattice 



This work is supported by the China Postdoctoral Science Foundation (2013M540837), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20121102130001) and the National Natural Science Foundation of China (61103097).


  1. 1.
    Feynman, R.P.: Simulating physics with computer. Int. J. Theor. Phys. 21, 467–488 (1982)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Deutsch, D.: Quantum theory, the Church-Turing principle and the universal quantum computer. Proc. R. Soc. Lond. A400, 97–117 (1985)MathSciNetCrossRefADSGoogle Scholar
  3. 3.
    Deutsch, D.: Quantum computational networks. Proc. Soc. Lond. A425, 73–90 (1989)MathSciNetCrossRefADSGoogle Scholar
  4. 4.
    Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science, pp. 124–134. IEEE Computer Society Press, Los Almitos, CA (1994)Google Scholar
  5. 5.
    Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on Theory of Computing, pp. 212–219. ACM, New York (1996)Google Scholar
  6. 6.
    Divincenzo, D.P.: Two-bit gates are universal for quantum computation. Phy. Rev. A 50, 1015 (1995)CrossRefADSGoogle Scholar
  7. 7.
    Aharonov, D., Van Dam, W., Kempe, J. Landau, Z., Lloyd, S., Regev, O.: Adiabatic quantum computation is equivalent to standard quantum computation. In: Proceedings of the 45th FOCS, pp. 42–45 (2004)Google Scholar
  8. 8.
    Childs, A.M.: Universal computation by quantum walk. Phys. Rev. Lett. 102, 80501 (2009)MathSciNetCrossRefADSGoogle Scholar
  9. 9.
    Bennett, C.H., Divincenzo, D.P.: Quantum information and computation. Nature 404, 247–255 (2000)CrossRefADSGoogle Scholar
  10. 10.
    Venegas-Andraca, S.E.: Quantum walks: a comprehensive review. Quantum Inf. Process. 11, 1015–1106 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Shen, J.-Q.: Gain-assisted negative refractive index in a quantum coherent medium. Progress Electromagn. Res. 133, 37–51 (2013)CrossRefGoogle Scholar
  12. 12.
    Monroe, C., Kim, J.: Scaling the ion trap quantum processor. Science 339, 1164–1169 (2013)CrossRefADSGoogle Scholar
  13. 13.
    Black, P.E., Kuhn, D.R., Williams, C.J.: Quantum computing and communication. Adv. Comput. 56, 189–244 (2002)CrossRefGoogle Scholar
  14. 14.
    Williams, C.P., Clearwater, S.H.: Ultimate Zero and One: Computing at the Quantum Frontier. Springer, Berlin (2000)CrossRefGoogle Scholar
  15. 15.
    Nielsen, M.A., Chuang I. L.: Quantum Computation and Quantum Information. Cambridge University, Cambridge (2000)Google Scholar
  16. 16.
    Tseng, C.C., Ming T.: Quantum digital image processing algorithms. In: 16th IPPR Conference on Computer Vision, Graphics and Image Processing, pp. 827–834, Kinmen, ROC (2003)Google Scholar
  17. 17.
    Latorre, J.I.: Image Compression and Entanglement. arXiv: quant-ph/0510031 (2003)Google Scholar
  18. 18.
    Venegas-Andraca, S.E., Bose S.: Storing, processing and retrieving an image using quantum mechanics. In: AeroSense 2003, International Society for Optics and Photonics, pp. 137–147 (2003)Google Scholar
  19. 19.
    Venegas-Andraca, S.E., Ball, J.L.: Processing image in entangled quantum systems. Quantum Inf. Process. 9(1), 1–11 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Le, P.Q., Dong, F-y, Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression and processing operations. Quantum Inf. Process. 10, 63–84 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Bennett, C.H., Divincenzo, D.P.: Quantum information and computation. Nature 404, 247–255 (2000)CrossRefADSGoogle Scholar
  22. 22.
    Wiesner, S.: Simulations of Many-Body Quantum Systems by a Quantum Computer. arXiv, preprint quant-ph/9603028, 110 (1996)Google Scholar
  23. 23.
    Abrans, D.S., Lloyd, S.: Simulations of many-body Hermi systems on a universal quantum computer. Phys. Rev. Lett. 79, 2586–2589 (1997)CrossRefADSGoogle Scholar
  24. 24.
    Cochran, W.G.: Sampling Techniques. Wiley, USA (1978)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Suzhen Yuan
    • 1
    • 2
  • Xia  Mao
    • 1
  • Yuli Xue
    • 1
  • Lijiang Chen
    • 1
  • Qingxu Xiong
    • 1
  • Angelo Compare
    • 3
  1. 1.School of Electronic and Information EngineeringBeihang UniversityBeijingChina
  2. 2.School of Physics and Electrical EngineeringAnyang Normal UniversityAnyangChina
  3. 3.Department of Human SciencesBergamo UniversityBergamoItaly

Personalised recommendations