000			03442cam a22004098a 4500
001			000q0313
003			WSP
005			20240731095159.0
007			cr |nu|||unuuu
008			210915s2022 si ob 001 0 eng
010			_a 2021035837
040			_aWSPC _beng _cWSPC
020			_a9781800610620 _q(ebook)
020			_a1800610629 _q(ebook)
020			_z9781800610613 _q(hbk.)
020			_z1800610610 _q(hbk.)
050	0	0	_aQA76.889 _b.S92 2022
072		7	_aCOM _x097000 _2bisacsh
072		7	_aCOM _x044000 _2bisacsh
072		7	_aMED _x057000 _2bisacsh
082	0	0	_a006.3/843 _223
100	1		_aSwan, Melanie _9178278
245	1	0	_aQuantum computing for the brain _h[electronic resource] / _cMelanie Swan ... [et al.].
260			_aSingapore : _bWorld Scientific, _c2022.
300			_a1 online resource (552 p.).
490	0		_aBetween science and economics, _x2051-6304 ; _v3
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction 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.
520			_a"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"-- _cProvided by publisher.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
650		0	_aQuantum computing. _910080
650		0	_aNeurosciences. _924499
655		0	_aElectronic books. _93294
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/q0313#t=toc _zAccess to full text is restricted to subscribers.
942			_cEBK
999			_c97732 _d97732