• Vladimir G. IvancevicEmail author
  • Tijana T. Ivancevic
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 40)


Introduction gives a glimpse of the quantum neural computation methods and techniques described in later the book. It includes various forms of quantum computation, quantum neural networks and quantum brain models, together with modern adaptive path-integral methods. Its main idea is a combination of quantum linearity and brain-like nonlinearity. It includes the following sections:

  1. 1.1


    This section gives a brief introduction to classical neural dynamical systems.

  2. 1.2

    Quantum Computation

    This section gives a brief introduction to quantum computation.

  3. 1.3

    Discrete Quantum Computers

    This section gives a historical overview of quantum computers.

  4. 1.4

    Topological Quantum Computers

    This section gives an overview of modern topological quantum computers.

  5. 1.5

    Computation at the Edge of Chaos and Quantum Neural Networks

    This section elaborates on the computation at the edge of chaos and introduces quantum neural networks.

  6. 1.6

    Adaptive Path Integral: An Infinite-Dimensional Quantum Neural Network

    This section introduces the concept of an adaptive path integral, the most powerful neural computation tool.

  7. 1.7

    Brain Topology vs. Small-World Topology

    This section compares and contrasts topological characteristics of neural systems versus social small-world networks.

  8. 1.8

    Quantum Brain and Mind

    This section introduces the modern concepts of quantum brain and quantum mind, from the point of view of: neural networks, biophysics, quantum neurodynamics and perception theory.

  9. 1.9

    Notational Conventions

    This section introduces notational conventions used in the book.



Partition Function Quantum Computer Central Processing Unit Braid Group Quantum Gate 
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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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of DefenceDefence Science & Technology Organisation (DSTO)EdinburghAustralia
  2. 2.School of Electrical & Information EngineeringUniversity of South AustraliaAdelaideAustralia

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