Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-understood, as of peak interest, and as in need of three-dimensional modeling as it functions in real-life, as the brain.

Quantum computing has emerged as a platform suited to contemporary data processing needs, surpassing classical computing and supercomputing. This book shows how quantum computing's increased capacity to model classical data with quantum states and the ability to run more complex permutations of problems can be employed in neuroscience applications such as neural signaling and synaptic integration. State-of-the-art methods are discussed such as quantum machine learning, tensor networks, Born machines, quantum kernel learning, wavelet transforms, Rydberg atom arrays, ion traps, boson sampling, graph-theoretic models, quantum optical machine learning, neuromorphic architectures, spiking neural networks, quantum teleportation, and quantum walks.

Quantum Computing for the Brain is a comprehensive one-stop resource for an improved understanding of the converging research frontiers of foundational physics, information theory, and neuroscience in the context of quantum computing.

Contents:

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  Introduction to Quantum Neuroscience

  
  


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  Foundations:

  
  
  
  
  • Neural Signaling Basics
  
  
  • The AdS/Brain Correspondence
  
  
  • Tabletop Experiments
  
  
  • Neuronal Gauge Theory
  
  
  
  


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  Substrate:

  
  
  
  
  • Quantum Information Theory
  
  
  • Quantum Computing 101
  
  
  • Glia Neurotransmitter Synaptome
  
  
  • Black Hole Information Theory
  
  
  
  


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  Connectivity:

  
  
  
  
  • Quantum Photonics and High-Dimensional Entanglement
  
  
  • Optical Machine Learning and Quantum Networks
  
  
  • Connectome and Brain Imaging
  
  
  • Brain Networks
  
  
  
  


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  System Evolution:

  
  
  
  
  • Quantum Dynamics
  
  
  • Neural Dynamics
  
  
  
  


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  Modeling Toolkit:

  
  
  
  
  • Quantum Machine Learning
  
  
  • Born Machine and Pixel = Qubit
  
  
  • Quantum Kernel Learning and Entanglement Design
  
  
  • Brain Modeling and Machine Learning
  
  
  • Conclusion: AdS/Brain Theory and Quantum Neuroscience
  
  
  
  

Readership: Thought-leaders, executives, industry strategists, research scientists, graduate students, advanced undergraduate students, policy-makers, research funding agencies, private research institutions, government regulators, investors, corporate managers, purchasing agents, and entrepreneurs in the areas of computer science, quantum computing, information theory, neuroscience, and physics.

Key Features:

• Inaugurate the field of quantum computing for the brain, providing groundbreaking research on novel applications of quantum computing for the study of the brain (specifically neural signalling)


• Provide groundbreaking coverage of the brain as one of the most enduring topics of interest and investigation. This is the first application of the AdS/CFT correspondence outside physics and information theory to neuroscience, the study of the brain, and neural signalling


• Valuable as a textbook for a semester or year-long course in quantum computing for the brain as well as courses in quantum network management in general


• Each chapter contains a standalone module with end-of-chapter references


• Part of the World Scientific series Between Science and Economics that examines conceptual models of classical, digital, and quantum economic exchange in a variety of contexts


• The authors are a cadre of global experts in emerging science and technology in physics, neuroscience, and economic models


• Quantum disciplines are beginning to go mainstream (beyond the initial tier of cryptography and quantum chemistry applications). Quantum finance is a fledgling field and quantum neuroscience is just on the cusp of innovation and development