Skip to main content
Log in

Image Compression by Layered Quantum Neural Networks

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

We have proposed the qubit neuron model as a new scheme in non-standard computing. Identification problems have been investigated on neural networks constructed by this qubit neuron model, and we have found high processing abilities of them. In this paper, we evaluate the performance of the quantum neural network of large size in image compression problems to estimate the utility for the practical applications comparing with the conventional network consists of formal neuron model.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Matsui, N., Takai, M. and Nishimura, H.: A network model based on qubit-like neuron corresponding to quantum circuit, IEICE, J81-A (12) (1998), 1687–1692. (in Japanese). Electronics and Communications in Japan, Part 3, 83 (10) (2000), 67-73.

    Google Scholar 

  2. Kak, S. C.: On quantum neural computing, Information Science, 83 (March 1995), 143–163.

    Google Scholar 

  3. Perus, M.: Neuro-Quantum Parallelism in Brain-Mind and Computers, Informatica 20 (1996), 173–183.

    Google Scholar 

  4. Matsui, N., Takai, M. and Nishimura, H.: A learning network based on qubit-like neuron model, Proceedings of the Seventeenth IASTED International Conference on Applied Informatics, (1999), 679–682

  5. Kouda, N., Matsui, N. and Nishimura, H.: Learning performance of neuron model based on quantum superposition, Proceedings of the 2000 IEEE International Workshop on Robot and Human Interactive Communication, (2000), 112–117.

  6. Cottrell, G. W., Munro, P. and Zipser, D.: Learning internal representations from greyscale images: An example of extensional programming, Proceedings of the 9th Annual Conference of the Cognitive Science Society, (1987), 461–473.

  7. Cottrell, G. W., Munro, P. and Zipser, D.: Image Compression by Back Propagation: An Example of External Programming, ICS Rep 8702, Institute for Cognitive Science, Univ. of California, San Diego, 1987.

  8. DiVincenzo, D. P.: Quantum computation, Science, 270 (October 1995), 255-261. Bennet, C. H., DiVincenzo, D. P.: Quantum information and computation, Nature, 404 (2000), 247–255.

    Google Scholar 

  9. Rumelhart, D. E., Hinton, G. E., Williams, R. J.: Learning internal representations by error propagation, In: D. E. Rumelhart, J. L. McClelland (eds.), Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Cambridge, MA: MIT Press, 1 (1986), 318–362.

    Google Scholar 

  10. Knill, E., Laflamme, R. and Milburn, G. J.: A scheme for efficient quantum computation with linear optics, Nature, 409 (2001), 46–52.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kouda, N., Matsui, N. & Nishimura, H. Image Compression by Layered Quantum Neural Networks. Neural Processing Letters 16, 67–80 (2002). https://doi.org/10.1023/A:1019708909383

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1019708909383

Navigation