This book presents an introduction, both non-technical and technical, to modern quantum neural computation. It combines quantum computation with neural computation and provides a blueprint for the future quantum brain.
Quantum Neural Computation is a graduatelevel monographic textbook. It presents a comprehensive introduction, both non-technical and technical, into modern quantum neural computation, the science behind the fiction movie Stealth. Classical computing systems perform classical computations (i.e., Boolean operations, such as AND, OR, NOT gates) using devices that can be described classically (e.g., MOSFETs). On the other hand, quantum computing systems perform classical computations using quantum devices (quantum dots), that is devices that can be described only using quantum mechanics. Any information transfer between such computing systems involves a state measurement. This book describes this information transfer at the edge of classical and quantum chaos and turbulence, where mysterious quantum-mechanical linearity meets even more mysterious brain's nonlinear complexity, in order to perform a superhighspeed and errorfree computations. This monograph describes a crossroad between quantum field theory, brain science and computational intelligence.
State-of-the-art in quantum neural computation
The first to combine quantum computation with neural computation
A blueprint for the future quantum brainKlappentext
Quantum Neural Computation is a graduate-level monographic textbook. It presents a comprehensive introduction, both non-technical and technical, into modern quantum neural computation. Classical computing systems perform classical computations (i.e., Boolean operations, such as AND, OR, NOT gates) using devices that can be described classically (e.g., MOSFETs). On the other hand, quantum computing systems perform classical computations using quantum devices (quantum dots), that is, devices that can be described only using quantum mechanics. Any information transfer between such computing systems involves a state measurement.
This book describes this information transfer at the edge of classical and quantum chaos and turbulence, where mysterious quantum-mechanical linearity meets the brain's even more mysterious nonlinear complexity, in order to perform superhighspeed and errorfree computations. This volume describes a crossroad between quantum field theory, brain science and computational intelligence.Inhalt
1 Introduction 1.1 Neurodynamics 1.2 Quantum Computation 1.3 Discrete Quantum Computers 1.4 Topological Quantum Computers 1.5 Computation at the Edge of Chaos and Quantum Neural Networks 1.6 Adaptive Path Integral: An 1-Dimensional QNN 1.6.1 Computational Partition Function 1.6.2 From Thermodynamics to Quantum Field Theory 1.6.3 1-Dimensional QNNs 1.7 Brain Topology vs. SmallWorld Topology 1.8 Quantum Brain and Mind 1.8.1 Connectionism, Control Theory and Brain Theory 1.8.2 Neocortical Biophysics 1.8.3 Quantum Neurodynamics 1.8.4 Bi-Stable Perception and Consciousness 1.9 Notational Conventions 2 Brain and Classical Neural Networks 2.1 Human Brain 2.1.1 Basics of Brain Physiology 2.1.2 Modern 3D Brain Imaging 2.2 Biological versus Artificial Neural Networks 2.2.1 Common Discrete ANNs 2.2.2 Common Continuous ANNs 2.3 Synchronization in Neurodynamics 2.3.1 Phase Synchronization in Coupled Chaotic Oscillators 2.3.2 Oscillatory Phase Neurodynamics 2.3.3 Kuramoto Synchronization Model 2.3.4 Lyapunov Chaotic Synchronization 2.4 Spike Neural Networks and Wavelet Resonance 2.4.1 Ensemble Neuron Model 2.4.2 Wavelet Neurodynamics 2.4.3 Wavelets of Epileptic Spikes 2.5 Human Motor Control and Learning 2.5.1 Motor Control 2.5.2 Human Memory 2.5.3 Human Learning 2.5.4 Spinal MusculoSkeletal Control 2.5.5 Cerebellum and Muscular Synergy 3 Quantum Theory Basics 3.1 Basics of Non-Relativistic Quantum Mechanics 3.1.1 Soft Introduction to Quantum Mechanics 3.1.2 Quantum States and Operators 3.1.3 The Tree Standard Quantum Pictures 3.1.4 Dirac's Probability Amplitude and Perturbation 3.1.5 StateSpace for n Non-Relativistic Quantum Particles 3.2 Introduction to Quantum Fields 3.2.1 Amplitude, Relativistic Invariance and Causality 3.2.2 Gauge Theories 3.2.3 Free andInteracting Field Theories 3.2.4 Dirac's Quantum Electrodynamics (QED) 3.2.5 Abelian Higgs Model 3.2.6 Topological Quantum Computation 3.3 The Feynman Path Integral 3.3.1 The ActionAmplitude Formalism 3.3.2 Correlation Functions and Generating Functional 3.3.3 Quantization of the Electromagnetic Field 3.3.4 WaveletBased QFT 3.4 The PathIntegral TQFT 3.4.1 SchwarzType and WittenType Theories 3.4.2 Hodge Decomposition Theorem 3.4.3 Hodge Decomposition and ChernSimons Theory 3.5 Non-Abelian Gauge Theories 3.5.1 Introduction to Non-Abelian Theories 3.5.2 YangMills Theory 3.5.3 Quantization of YangMills theory 3.5.4 Basics of Conformal Field Theory (CFT) 4 Spatio-Temporal Chaos, Solitons and NLS 4.1 ReactionDiffusion Processes and Ricci Flow 4.1.1 BioReactionDiffusion Systems 4.1.2 Reactive Neurodynamics 4.1.3 Dissipative Evolution Under the Ricci Flow 4.2 Turbulence and Chaos in PDEs 4.3 Quantum Chaos and Its Control 4.3.1 Quantum Chaos vs. Classical Chaos 4.3.2 Optimal Control of Quantum Chaos 4.4 Solitions 4.4.1 Short History of Solitons 4.4.2 LiePoisson Bracket 4.4.3 Solitons and Muscular Contraction 4.5 Dispersive Wave Equations and Stability of Solitons 4.5.1 KdV Solitons 4.5.2 The Inverse Scattering Approach 4.6 Nonlinear Schr¨odinger Equation (NLS) 4.6.1 Cubic NLS 4.6.2 Nonlinear Wave and Schr¨odinger Equations 4.6.3 Physical NLSDerivation 4.6.4 A Compact Attractor for HighDimensional NLS 4.6.5 FiniteDifference Scheme for NLS 4.6.6 Method of Lines for NLS 5 Quantum Brain and Cognition 5.1 Biochemistry of Microtubules 5.2 Kink Soliton Model of MTDynamics 5.3 Macro and Microscopic Neurodynamical SelfSimilarity 5.3.1 Open Liouville Equation 5.4 Dissipative Quantum Brain Model 5.5 QED Brain Model 5.6