大脑的量子计算 Quantum Computing For The Brain
基本信息
Publisher：World Scientific Publishing Europe Ltd (June 22, 2022)
Language：English
Hardcover：552 pages
ISBN-10：1800610610
ISBN-13：978-1800610613
Item Weight：1.99 pounds
Dimensions：6 x 1.25 x 9 inches
页面参数仅供参考，具体以实物为准
书籍简介
《大脑的量子计算》认为，大脑是量子计算的杀手级应用。没有任何一个系统像大脑那样复杂，在时间和空间上是多维的，是动态的，是不太了解的，是令人感兴趣的，是需要在现实生活中进行三维建模的。
量子计算已经成为适合当代数据处理需求的平台，超越了经典计算和超级计算。本书展示了量子计算增加的用量子态模拟经典数据的能力和运行更复杂的问题排列的能力如何在神经科学应用中得到应用，如神经信号和突触整合。书中讨论了先进的方法，如量子机器学习、张量网络、Born机器、量子核学习、小波变换、Rydberg原子阵列、离子阱、玻色子采样、图论模型、量子光学机器学习、神经形态架构、尖峰神经网络、量子传送和量子漫步。
大脑的量子计算是一个全面的一站式资源，以提高对量子计算背景下基础物理学、信息论和神经科学的融合研究前沿的理解。
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.
作者简介
By (author): Melanie Swan (University College London, UK), Renato P dos Santos (University of Brazil, Brazil), Mikhail Lebedev (National Research University Higher School of Economics, Russia), and Frank Witte (University College London, UK)
Melanie Swan是伦敦大学学院区块链技术中心的研究助理。她拥有普渡大学的博士学位，宾夕法尼亚大学沃顿商学院的MBA，以及乔治敦大学的学士学位。她的研究兴趣是新兴科学技术对生物学的支持，她开创了基因组学的众包健康研究，从事脑云界面研究，并在光网络精品公司Ovum-RHK领导电信经济项目。她是伦理与新兴技术研究所的附属学者，也是FQXi基础物理学论文竞赛的参赛者。她是畅销书《区块链》的作者： 新经济蓝图》的作者，以及另外两本书《区块链经济学》和《量子计算》的共同作者： 物理学、区块链和深度学习智能网络。
Melanie Swan is a Research Associate at the Centre for Blockchain Technologies at University College London. She has a PhD from Purdue University, an MBA from the Wharton School of the University of Pennsylvania, and a BA from Georgetown University. Previous academic affiliations include Kingston University London and Université Paris 8. With research interests in emerging science and technology supporting biology, she pioneered the crowdsourced health research study in genomics, works on brain-cloud interface research, and led the Telecom Economics Program at optical networking boutique Ovum-RHK. She is an Affiliate Scholar with the Institute for Ethics and Emerging Technologies and a participant in FQXi's foundational physics Essay Contest. She is the author of the best-selling book Blockchain: Blueprint for a New Economy, and coauthor of two other books, Blockchain Economics, and Quantum Computing: Physics, Blockchains, and Deep Learning Smart Networks.
目录
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
