Deep Learning and Neuromorphic Computing in Quantum Chromodynamics and Beyond PDF Download
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Author: Lukas Kades
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Lukas Kades
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Melanie Swan
Publisher: World Scientific
ISBN: 1800610637
Category : Computers
Languages : en
Pages : 551
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Book Description
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.
Author: Khaled Salah Mohamed
Publisher:
ISBN: 9783030372255
Category : Neural computers
Languages : en
Pages : 233
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Book Description
This book discusses and compares several new trends that can be used to overcome Moore's law limitations, including Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing. The author shows how these paradigms are used to enhance computing capability as developers face the practical and physical limitations of scaling, while the demand for computing power keeps increasing. The discussion includes a state-of-the-art overview and the essential details of each of these paradigms. Introduces in one volume all the trends that can be used to overcome Moore's law limitations; Describes in detail Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing concepts, in a manner accessible to a wide variety of readers; Compares tradeoffs between the various paradigms discussed.
Author: Claudio Conti
Publisher: Springer Nature
ISBN: 3031442261
Category : Science
Languages : en
Pages : 393
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Book Description
This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning.
Author:
Publisher: BoD – Books on Demand
ISBN: 1803561432
Category : Computers
Languages : en
Pages : 298
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Book Description
Dive into the cutting-edge world of Neuromorphic Computing, a groundbreaking volume that unravels the secrets of brain-inspired computational paradigms. Spanning neuroscience, artificial intelligence, and hardware design, this book presents a comprehensive exploration of neuromorphic systems, empowering both experts and newcomers to embrace the limitless potential of brain-inspired computing. Discover the fundamental principles that underpin neural computation as we journey through the origins of neuromorphic architectures, meticulously crafted to mimic the brain’s intricate neural networks. Unlock the true essence of learning mechanisms – unsupervised, supervised, and reinforcement learning – and witness how these innovations are shaping the future of artificial intelligence.
Author: Pethuru Raj
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3111342271
Category : Computers
Languages : en
Pages : 336
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Book Description
Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.
Author: Vladimir G. Ivancevic
Publisher: Springer Science & Business Media
ISBN: 9048133505
Category : Computers
Languages : en
Pages : 938
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Book Description
Quantum Neural Computation is a graduate–level 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 super–high–speed and error–free computations. This monograph describes a crossroad between quantum field theory, brain science and computational intelligence.
Author: Huajin Tang
Publisher: Frontiers Media SA
ISBN: 283255363X
Category : Science
Languages : en
Pages : 152
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Book Description
Towards the long-standing dream of artificial intelligence, two solution paths have been paved: (i) neuroscience-driven neuromorphic computing; (ii) computer science-driven machine learning. The former targets at harnessing neuroscience to obtain insights for brain-like processing, by studying the detailed implementation of neural dynamics, circuits, coding and learning. Although our understanding of how the brain works is still very limited, this bio-plausible way offers an appealing promise for future general intelligence. In contrast, the latter aims at solving practical tasks typically formulated as a cost function with high accuracy, by eschewing most neuroscience details in favor of brute force optimization and feeding a large volume of data. With the help of big data (e.g. ImageNet), high-performance processors (e.g. GPU, TPU), effective training algorithms (e.g. artificial neural networks with gradient descent training), and easy-to-use design tools (e.g. Pytorch, Tensorflow), machine learning has achieved superior performance in a broad spectrum of scenarios. Although acclaimed for the biological plausibility and the low power advantage (benefit from the spike signals and event-driven processing), there are ongoing debates and skepticisms about neuromorphic computing since it usually performs worse than machine learning in practical tasks especially in terms of the accuracy.
Author: Kezhou Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In the past decade artificial intelligence has undergone vast development thanks to deep learning techniques. However, the large computation overhead limits the application of AI in scenarios where area and energy consumption are limited. This is due to the mismatch in architecture between von Neumann hardware computing systems and deep learning algorithms. As a promising solution to the problem, neuromorphic computing has attracted great research interest. While there are efforts to build neuromorphic computing systems based on CMOS technology, memristors which provide intrinsic dynamics similar to synapses and neurons are also under exploration. Among different types of memristors, this dissertation focus on spintronic devices, which offer more plentiful neural or synaptic functionalities with a low operating voltage. The work in this dissertation consists of both simulation and experimental part. On simulation side, a stochastic neuron design based on magnetic tunnel junction utilizing magnetic-electro effect is proposed. The stochastic neurons are used to build spiking neural networks, which show improved spike sparsity with good test accuracy. Apart from spiking neural network, an all-spin Bayesian neural network is proposed, where intrinsic stochasticity of scaled devices is utilized for random number generation. Voltage controlled magnetic anisotropy effect-based magnetic tunnel junction is explored and utilized to solve write sneak path problem in crossbar array structure. On experiment side, Hall bars are fabricated on ferromagnetic/heavy metal materials stacks and utilized as neurons. Relations between Hall bar characteristics and size are explored. Hardware-in-loop training has been studied with Hall bar neurons.
Author: Pethuru Raj
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110791471
Category : Computers
Languages : en
Pages : 0
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Book Description
This book is to explore and explain the strategically sound capabilities at the synchronization between quantum computing and artificial intelligence (AI). The reader will be presented with an introduction and a deeper review of the technological trends and transitions being unearthed in the quantum computing and AI domains.
