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Proposal
Name
Institution
Proposal
Background
Introduction
A lot of what comes under the field of artificial intelligence is not creating a general intelligence that can solve problems in any domain as a human does. However, a domain-specific problem can be solved using artificial intelligence in the best possible manner. The impact of a quantum computer on domain-specific problems is that, instead of testing out every possible branch one after the other until one gets a solution, quantum computing could explore every branch of the problem simultaneously. This paper will discuss the role of quantum computing on improving the performance of artificial intelligence. The paper will focus on; thinking through the implications of quantum mechanics, and the machines that are useful for the quantum computation in order to improve the performance of artificial intelligence.
What is Artificial Intelligence and Quantum Computing?
Artificial Intelligence
Artificial intelligence serves the purpose of creating machines, that are not only intelligent but work and react like humans. The main problem that is associated with artificial intelligence involves computer programming for planning, learning, reasoning, knowledge, problem-solving, perception, etc.
Quantum Computing
Albeit the normal computing owes quantum effects and functions just like normal computing, however, the only thing that distinguishes quantum computing from normal computing is the use of quantum bits. Quantum computing is exponentially faster than normal computing. Some of the important conventional problems can be solved in exponentially less time by using quantum algorithms.
Plan
Literature Review
Literature review Strategy
Selection of related peer-reviewed articles
Literature review on classical or conventional computing
Literature review on quantum computing and its role in improving the performance of artificial intelligence.
Comparison between classical computing and quantum computing.
The role played by classical computing in improving artificial intelligence.
Comparison of the past researches on classical computing and quantum computing and the role both computing techniques have played in improving artificial intelligence.
Summary
Reason and Conclusion
Research gap in the existing literature and this research.
The research aims at filling the research gap on quantum computing and its role vis-a-vis the performance of artificial intelligence.
The research is important as it focuses not only the implications of quantum computing but also on the machine that is useful for quantum computing.
The research is important because it discusses the domain-specific problem and their solution using artificial intelligence and quantum computing.
The research will explore the relationship between quantum computing and artificial intelligence.
The research is important as it focuses on what kind of domain-specific problem can be solved by quantum computing and artificial intelligence.
The paper directs researcher, the pave way for numerical optimization using quantum computing and artificial intelligence.
References
ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Aaronson, S., & Ambainis, A. (2018). Forrelation: A problem that optimally separates quantum from classical computing. SIAM Journal on Computing, 47(3), 982–1038.
Manin, Y. I. (2000). Classical computing, quantum computing, and Shor’s factoring algorithm. ASTERISQUE-SOCIETE MATHEMATIQUE DE FRANCE, 266, 375–404.
Miakisz, K., Piotrowski, E. W., & S\ladkowski, J. (2006). Quantization of games: Towards quantum artificial intelligence. Theoretical Computer Science, 358(1), 15–22.
Venegas-Andraca, S. E., & Bose, S. (2003). Quantum computation and image processing: New trends in artificial intelligence. IJCAI, 1563.
Ying, M. (2010). Quantum computation, quantum theory and AI. Artificial Intelligence, 174(2), 162–176.
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