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Author: George Chapline
Publisher: World Scientific
ISBN: 981323248X
Category : Computers
Languages : en
Pages : 194
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Book Description
This compendium brings together the fields of Quantum Computing, Machine Learning, and Neuromorphic Computing. It provides an elementary introduction for students and researchers interested in quantum or neuromorphic computing to the basics of machine learning and the possibilities for using quantum devices for pattern recognition and Bayesian decision tree problems. The volume also highlights some possibly new insights into the meaning of quantum mechanics, for example, why a description of Nature requires probabilistic rather than deterministic methods.
Author: George Chapline
Publisher: World Scientific
ISBN: 981323248X
Category : Computers
Languages : en
Pages : 194
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Book Description
This compendium brings together the fields of Quantum Computing, Machine Learning, and Neuromorphic Computing. It provides an elementary introduction for students and researchers interested in quantum or neuromorphic computing to the basics of machine learning and the possibilities for using quantum devices for pattern recognition and Bayesian decision tree problems. The volume also highlights some possibly new insights into the meaning of quantum mechanics, for example, why a description of Nature requires probabilistic rather than deterministic methods.
Author: Kristof T. Schütt
Publisher: Springer Nature
ISBN: 3030402452
Category : Science
Languages : en
Pages : 473
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Book Description
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Author: Hans Christian von Baeyer
Publisher: Harvard University Press
ISBN: 067450464X
Category : Science
Languages : en
Pages : 268
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Book Description
Measured by the accuracy of its predictions and the scope of its technological applications, quantum mechanics is one of the most successful theories in science—as well as one of the most misunderstood. The deeper meaning of quantum mechanics remains controversial almost a century after its invention. Providing a way past quantum theory’s paradoxes and puzzles, QBism offers a strikingly new interpretation that opens up for the nonspecialist reader the profound implications of quantum mechanics for how we understand and interact with the world. Short for Quantum Bayesianism, QBism adapts many of the conventional features of quantum mechanics in light of a revised understanding of probability. Bayesian probability, unlike the standard “frequentist probability,” is defined as a numerical measure of the degree of an observer’s belief that a future event will occur or that a particular proposition is true. Bayesianism’s advantages over frequentist probability are that it is applicable to singular events, its probability estimates can be updated based on acquisition of new information, and it can effortlessly include frequentist results. But perhaps most important, much of the weirdness associated with quantum theory—the idea that an atom can be in two places at once, or that signals can travel faster than the speed of light, or that Schrödinger’s cat can be simultaneously dead and alive—dissolves under the lens of QBism. Using straightforward language without equations, Hans Christian von Baeyer clarifies the meaning of quantum mechanics in a commonsense way that suggests a new approach to physics in general.
Author: Maria Schuld
Publisher: Springer Nature
ISBN: 3030830985
Category : Science
Languages : en
Pages : 321
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Book Description
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Author: Prateek Tandon
Publisher: Morgan & Claypool Publishers
ISBN: 1627059954
Category : Science
Languages : en
Pages : 151
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Book Description
Quantum robotics is an emerging engineering and scientific research discipline that explores the application of quantum mechanics, quantum computing, quantum algorithms, and related fields to robotics. This work broadly surveys advances in our scientific understanding and engineering of quantum mechanisms and how these developments are expected to impact the technical capability for robots to sense, plan, learn, and act in a dynamic environment. It also discusses the new technological potential that quantum approaches may unlock for sensing and control, especially for exploring and manipulating quantum-scale environments. Finally, the work surveys the state of the art in current implementations, along with their benefits and limitations, and provides a roadmap for the future.
Author: A. Stern
Publisher: Elsevier
ISBN: 0080540139
Category : Mathematics
Languages : en
Pages : 601
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Book Description
Making Sense of Inner Sense 'Terra cognita' is terra incognita. It is difficult to find someone not taken abackand fascinated by the incomprehensible but indisputable fact: there are material systems which are aware of themselves. Consciousness is self-cognizing code. During homo sapiens's relentness and often frustrated search for self-understanding various theories of consciousness have been and continue to be proposed. However, it remains unclear whether and at what level the problems of consciousness and intelligent thought can be resolved. Science's greatest challenge is to answer the fundamental question: what precisely does a cognitive state amount to in physical terms? Albert Einstein insisted that the fundamental ideas of science are essentially simple and can be expressed in a language comprehensible to everyone. When one thinks about the complexities which present themselves in modern physics and even more so in the physics of life, one may wonder whether Einstein really meant what he said. Are we to consider the fundamental problem of the mind, whose understanding seems to lie outside the limits of the mind, to be essentially simple too? Knowledge is neither automatic nor universally deductive. Great new ideas are typically counterintuitive and outrageous, and connecting them by simple logical steps to existing knowledge is often a hard undertaking. The notion of a tensor was needed to provide the general theory of relativity; the notion of entropy had to be developed before we could get full insight into the laws of thermodynamics; the notice of information bit is crucial for communication theory, just as the concept of a Turing machine is instrumental in the deep understanding of a computer. To understand something, consciousness must reach an adequate intellectual level, even more so in order to understand itself. Reality is full of unending mysteries, the true explanation of which requires very technical knowledge, often involving notions not given directly to intuition. Even though the entire content and the results of this study are contained in the eight pages of the mathematical abstract, it would be unrealistic and impractical to suggest that anyone can gain full insight into the theory that presented here after just reading abstract. In our quest for knowledge we are exploring the remotest areas of the macrocosm and probing the invisible particles of the microcosm, from tiny neutrinos and strange quarks to black holes and the Big Bang. But the greatest mystery is very close to home: the greatest mystery is human consciousness. The question before us is whether the logical brain has evolved to a conceptual level where it is able to understand itself.
Author: Kingsley Jones
Publisher: Kingsley Jones
ISBN: 146816449X
Category : Science
Languages : en
Pages : 162
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Book Description
Research in the Quantum Information Sciences has exploded since the 1980s, with fascinating results across new fields such as Quantum Cryptography, Quantum Computing and Quantum Information Theory. However, the deep connections between Quantum Measurement, Inductive Logic and the theory of Bayesian Inference have been comparatively less developed. In this work, a facsimile reprint of a 1989 University of Bristol PhD thesis, the reader encounters the formative stages of the theory of Quantum Bayesian Inference. Sections of this thesis were later published [KRW Jones: Annals of Physics, 207 140 (1991)], but the full text contains many previously unpublished computational tricks and asides that will likely be of interest to current workers in the field. It may also prove useful to those who study the historical process of how novel scientific ideas emerge, develop and cross-fertilize across diverse global research centers.
Author: N.B. Singh
Publisher: N.B. Singh
ISBN:
Category : Computers
Languages : en
Pages : 306
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Book Description
"From Schrödinger's Equation to Deep Learning: A Quantum Approach" offers a captivating exploration that bridges the realms of quantum mechanics and deep learning. Tailored for scientists, researchers, and enthusiasts in both quantum physics and artificial intelligence, this book delves into the symbiotic relationship between quantum principles and cutting-edge deep learning techniques. Covering topics such as quantum-inspired algorithms, neural networks, and computational advancements, the book provides a comprehensive overview of how quantum approaches enrich and influence the field of deep learning. With clarity and depth, it serves as an enlightening resource for those intrigued by the dynamic synergy between quantum mechanics and the transformative potential of deep learning.
Author: Maria Schuld
Publisher: Springer
ISBN: 3319964240
Category : Science
Languages : en
Pages : 293
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Book Description
Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Author: Christopher G. Timpson
Publisher: OUP Oxford
ISBN: 0191662909
Category : Philosophy
Languages : en
Pages : 308
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Book Description
Quantum Information Theory and the Foundations of Quantum Mechanics is a conceptual analysis of one the most prominent and exciting new areas of physics, providing the first full-length philosophical treatment of quantum information theory and the questions it raises for our understanding of the quantum world. Beginning from a careful, revisionary, analysis of the concepts of information in the everyday and classical information-theory settings, Christopher G. Timpson argues for an ontologically deflationary account of the nature of quantum information. Against what many have supposed, quantum information can be clearly defined (it is not a primitive or vague notion) but it is not part of the material contents of the world. Timpson's account sheds light on the nature of nonlocality and information flow in the presence of entanglement and, in particular, dissolves puzzles surrounding the remarkable process of quantum teleportation. In addition it permits a clear view of what the ontological and methodological lessons provided by quantum information theory are; lessons which bear on the gripping question of what role a concept like information has to play in fundamental physics. Topics discussed include the slogan 'Information is Physical', the prospects for an informational immaterialism (the view that information rather than matter might fundamentally constitute the world), and the status of the Church-Turing hypothesis in light of quantum computation. With a clear grasp of the concept of information in hand, Timpson turns his attention to the pressing question of whether advances in quantum information theory pave the way for the resolution of the traditional conceptual problems of quantum mechanics: the deep problems which loom over measurement, nonlocality and the general nature of quantum ontology. He marks out a number of common pitfalls to be avoided before analysing in detail some concrete proposals, including the radical quantum Bayesian programme of Caves, Fuchs, and Schack. One central moral which is drawn is that, for all the interest that the quantum information-inspired approaches hold, no cheap resolutions to the traditional problems of quantum mechanics are to be had.
