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Quantum computing for the brain [electronic resource] / Melanie Swan ... [et al.].

By: Swan, Melanie.
Material type: materialTypeLabelBookSeries: Between science and economics, 3.Publisher: Singapore : World Scientific, 2022Description: 1 online resource (552 p.).ISBN: 9781800610620; 1800610629.Subject(s): Quantum computing | NeurosciencesGenre/Form: Electronic books.DDC classification: 006.3/843 Online resources: Access to full text is restricted to subscribers.
Contents:
Introduction to quantum neuroscience - Foundations. Neural signaling basics -- The ads/brain correspondence -- Tabletop experiments -- Neuronal gauge theory - Substrate. Quantum information theory -- Quantum computing 101 -- Glia neurotransmitter -- synaptome -- Black hole information theory - Connectivity. Quantum photonics and high-dimensional entanglement -- Optical machine learning and quantum networks -- Connectome and brain imaging -- Brain networks -- System evolution. Quantum dynamics -- Neural dynamics -- 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.
Summary: "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"-- Provided by publisher.
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Includes bibliographical references and index.

Introduction to quantum neuroscience - Foundations. Neural signaling basics -- The ads/brain correspondence -- Tabletop experiments -- Neuronal gauge theory - Substrate. Quantum information theory -- Quantum computing 101 -- Glia neurotransmitter -- synaptome -- Black hole information theory - Connectivity. Quantum photonics and high-dimensional entanglement -- Optical machine learning and quantum networks -- Connectome and brain imaging -- Brain networks -- System evolution. Quantum dynamics -- Neural dynamics -- 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.

"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"-- Provided by publisher.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat reader.

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