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Quantum Mechanics and Artificial Intelligence

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This chapter presents the case that quantum mechanical machines will be needed for AI (artificial intelligence) to match biological intelligence. We begin with an overview of quantum mechanics and describe some of its paradoxes related to nonlocality and instantaneous reduction of the wave function. The case is made that the nonlocality of quantum mechanics and the probabilistic state reduction upon measurement make the theory noncomputable in the classical sense. Some parallels between quantum processing and the workings of the human mind are sketched. Biological systems at the physical level as well as the brain are characterized by reorganization in response to stimulus, which is clearly seen in the changing of the strength of interconnection between neurons, indicating that biological learning is very different from classical machine learning. But reorganization by itself cannot be the reason behind the power of biological intelligence, and therefore we examine the recently proposed quantum computing models for their computing power. We provide an overview of their functioning and we also critique them from the point of view of their realizability. We argue that practical quantum machines will have to be conceived differently from those presently being researched.

Keywords

  • Wave Function
  • Quantum State
  • Unitary Transformation
  • Circuit Model
  • Quantum Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Kak, S. (2007). Quantum Mechanics and Artificial Intelligence. In: Schuster, A.J. (eds) Intelligent Computing Everywhere. Springer, London. https://doi.org/10.1007/978-1-84628-943-9_5

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  • DOI: https://doi.org/10.1007/978-1-84628-943-9_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-942-2

  • Online ISBN: 978-1-84628-943-9

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