A Review on Quantum Vision Intelligent Learning (QuVIL) System

Authors

  • Chowdhury DR Department of Computer Science & Application, University of North Bengal, Raja Rammohunpur West Bengal, India
  • Bhattacharya S Calcutta Institute of Technology West Bengal University of Technology West Bengal, India

Keywords:

Quantum Theory, Quantum Computing, Machine Learning, Computer Vision, Artificial Intelligence

Abstract

The dimension of Artificial Intelligence is vast and this keeps on increasing when new methodologies are incorporated in it. The main purpose of this paper is to exploit the potential Application of Quantum computing in AI. Quantum Artificial Intelligence helps in solving some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. Using Quantum AI with Bioinformatics, we can get such an automata that will analyze the symptoms of patients and draw better models of how diseases develop and how to cure them. Application of QuAI in Space Research is endless. Quantum AI with Cryptanalysis can help to create more secure algorithms for Information security. So there are endless positive possibilities if application of Quantum Artificial Intelligence is applied correctly.

References

Anya Tafliovich and Eric C. R. Hehner, “Programming with Quantum Communication”, Electronic Notes in Theoretical Computer Science 253 (2009) 99–118, Computer Science. University of Toronto, Toronto, Canada.

C. L. Chen, D. Y. Dong, Z. H. Chen, “Quantum Computation for Action Selection using Reinforcement Learning”, Volume 04, Issue 06, December 2006, Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230027, P. R. China.

D. Roy Chowdhury, M. Chatterjee, and R. K. Samanta, “NeuroGenetic fusion approach towards developing a Decision Support System for Neonatal Disease Diagnosis”, NaCCS, National Conference on Computing and Systems, Dept. of Computer Science., Burdwan University, W. B., India. pp. 242-248, 2012.

D.Conte, P. Foggia, C.Sasoneand M.Vento,“Thirty Years of Graph Matching InPatternRecognition”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 18, No.3, (2004), 265-298.

Mayank Parasher, Shruti Sharma, A.K Sharma, J. P. Gupta, “Anatomyon PatternRecognition”, Indian Journal of Computer Science and Engineering (IJCSE), ISSN: 0976-5166,Vol. 2 No. 3 Jun-Jul 2011.

Jie Liu, Jigui Sun, Shengsheng Wang, “Pattern Recognition: An overview”,IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.6, June 2006.

Fabian Pedregosa,Ga ̈el Varoquaux,Alexandre Gramfor,Vincent Michel,Bertrand, “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research 12 (2011) 2825-2830 ,Thirion,Parietal, INRIA Saclay Neurospin, Bˆat 145, CEA Saclay,91191 Gif sur Yvette –France.

Google, NASA and USRA Collaboration, “The Quantum Artificial Intelligence Lab(QuAIL)” .

Yong-Ki Kim (September 2, 2000). "Practical Atomic Physics". National Institute of Standards and Technology (Maryland): 1 (55 pages). Retrieved 2010-08-17.

Haiping Lu, K. N. Plataniotis and A. N. Venetsanopoulos, A Survey of Mul-tilinear Subspace Learning for Tensor Data", Pattern Recognition, vol. 44, no.7, pp. 1540-1551, Jul. 2011.

C. Monroe, D. M. Meekhof, B. E. King, W. M. Itano, and D. J. Wineland,” Demonstration of a Fundamental Quantum Logic Gate”,National Institute of Standards and Technology, Boulder, Colorado 80303(Received 14 July 1995).

Rybicki, G. B.; Lightman, A. P. (1979). Radiative Processes in Astrophysics. John Wiley & Sons. ISBN 0-471-82759-2.]

Tom M. Mitchell, “The Discipline of Machine Learning,July 2006,CMU-ML-06-108,School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213

Camilleri, K. (2006), “Heisenberg and the Wave-particle Duality”, in Studies in History and Philosophy of Modern Physics, 37: 298–315.

http://www.alanturing.net/turing_archive/pages/reference%20articles/what%20is%20ai.html

http://en.wikipedia.org/wiki/Machine_learning

http://googleresearch.blogspot.in/2013/05/launching-quantum-artificial.html

http://en.wikipedia.org/wiki/Quantum_Artificial_Intelligence_Lab

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Published

2015-02-28

How to Cite

[1]
D. R. Chowdhury and S. Bhattacharya, “A Review on Quantum Vision Intelligent Learning (QuVIL) System”, Int. J. Comp. Sci. Eng., vol. 3, no. 1, pp. 43–50, Feb. 2015.

Issue

Vol. 3 No. 1 (2015): IJCSE Special Issue February Edition

Section

Review Article

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