A New Economic Era in Computer Science: DNA Vs Quantum Computing
Abstract
The increasing rate of growth both of science and technology in our era, renders the forecasts for the development course of each technology, very significant for the strategic development designer of any organism, either this concerns an enterprise or a state or a union of states. Such a technology is the computer technology, which is currently to the limit of its current possibilities and is being prepared to pass into a new era and create a new economic environment. The silicon technology, because of its constructional restrictions, cannot maintain the existing growth rate for more than one decade. “Spintronics” or “molecular electronics” can constitute transient technologies, but the radical change will be the transition from serial to parallel calculating process.
Aim of this work is to constitute a useful tool for the forecast of development of the subversive technologies that will bring us to a new era of computer science. DNA computing and Quantum computing are considered as such technologies. The use of such a forecast may result in dramatic changes in the economic map of the companies engaged in information technology, since it will allow an early placement of them in upcoming competitive race.
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References
Adleman, L. M. (1994). Molecular computation of solutions to combinatorial problems. Science, 266 (5187): 1021–1024. doi:10.1126/science.7973651
Burgelman, R. A., Christensen, C. M., & Wheelright, S. C.,(2006). Strategic Management of Technology and Innovation, McGraw Hill Irwin, New York.
Bester, J., (2000). Takeover Theories and predictions models, Institute for Economic Research, Ljubljana, December 2000
Benioff, P. (1982). Quantum mechanical hamiltonian models of turing machines. Journal of Statistical Physics, 29 (3): 515–546. doi:10.1007/BF01342185
Debecker, A. & Modis, T. (1994). Determination of the Uncertainties in S-curve Logistic Fits, Tech. Forecasting and Social Change, 46, p. 153-173
Dussange, P. 7 Romanantsoa, B.,(1987). Technologie et Stratégie d'entreprise, MacGraw-Hill, Paris.
Georgalis, E.E. & Aifantis, E.C., (2013). Forecasting the evolution of nanotechnology, Nano Bulletin, 2, 2. ISSN 2159-0427
Geroski, P.,(2000). Models of technology diffusion, London Business School, April 2000, UK.
Intel Research - Silicon , (2001). Moore's Law, www.intel.com. accessed 13/6/2010
Kruse, R., Gebhardt, J., Klawon, F., (1994). Foundations of Fuzzy Systems, Wiley, Chichester
Kurzweil, R., (2005). The Singularity is near. When humans transcend biology. Penguin Books, London, England.
http://www.amazon.com/The-Singularity-Is-Near-Transcend dp/0143037889
Lehtonen, E., Tissari, J., Poikonen, J., Laiho, M. & Kosoinen, L., (2014), A cellular computing architecture for parallel memristive stateful logic. Microelectronics Journal, 45 (11), p.1438-1449
Li, X., Bhushan, B., Takashima, K., Baek, C.-W. and Kim, Y.-K., (2003), Mechanical characterization of micro/nanoscale structures for MEMS/NEMS applications using nanoindentation techniques, Ultramicroscopy, 97, (1-4), p. 481-494, October 2003.
Menon, P.S. & Ritwik, M. (2014). A Comprehensive but not Complicated Survey on Quantum Computing, IERI Procedia, 10, p.144-152
Modis, Th., (1992). Predictions - Society's Telltale Signature Reveals the Past and Forecasts the Future. Simon & Schuster, New York.
Nguyen, L.A. (2006). Multimodal logic programming, Theoretical Computer Science, 360 (1-3), p.247-288
Nieto, M., Lopez, F., Cruz, F., (1998). Performance analysis of technology using the S curve model:the case of digital signal processing (DSP) technologies, Technovation, 18, p.439-457.
Ogihara, M. & Ray, A. (1997). The Minimum DNA Computation Model and Its Computational Power. Technical Report, Univ. of Rochester,
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.4315&rep=rep1&type=pdf
Porter, M.E., (1980). Competitive Strategy , The Free Press: New York,NY.
Schumpeter, J., (1935). Business Cycles: A Theoritical, Historical and Statistical Analysis of the Capitalist Process, New York.
Tennant, D.M. & Bleier, A.R., (2011). Electron Beam Lithography of Nanostructures, Comprehensive Nanoscience and Technology, 4, p. 35-62
Turing, A.M. (1936). On Computable Numbers, with an Application to the Entscheidungs problem. Proceedings of the London Mathematical Society. 2, 42, 230–265. doi:10.1112/plms/s2-42.1.230
Verdaguer, M., and Robert, V., (2013). Fundamentals, Principles, and Concepts of Molecular Magnetism and Spintronics. Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, 8, p. 131-139
Cervantes-Salido, V.M., Jaime, O., Brizuela, C.A. & Martinez-Perez, I.M., (2013). Improving the design of sequences for DNA computing: A multiobjective evolutionary approach, Applied Soft Computing, 13 (12), p.4594-4607
Vuillaume, D., (2008). Molecular-scale electronics, Comptes Rendus Physique, 9 (1), p.78-94
Zhang, F., Liu, B., Liu, W. & Zhang, Q., (2008). DNA computing model based on lab-on-a-chip and its application on solving the timetabling problem, Progress in Natural Science, 18 (5), p. 633-637
Wittek, P. (2014). Quantum Computing, Quantum Machine Learning, Academic Press, ISBN: 978-0-12-800953-6
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