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Download or read book Training Binary Neural Networks with a Quantum Computer written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sabrina Ly
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Quantum Solutions for Training a Single Layer Binary Neural Network written by Sabrina Ly and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Quantum computing is a relatively new field starting in the early 1980s when a physicist named Paul Benioff proposed a quantum mechanical model of the Turing machine, introducing quantum computers. Previously, the focus of most quantum computers was in the study of quantum applications instead of broad applications due to the fact that quantum technology is a newer field with many technology constraints, such as limited qubits and noisy environments. However, quantum computers are still capable of using quantum mechanics to solve specific algorithms with an exponential speed-up in comparison to their classical counterparts. One key algorithm is the HHL algorithm proposed by Harrow, Hassidim and Lloyd in 2009 [1]. This algorithm outlines a quantum approach to solve a linear systems of equations with a best case time complexity of O(poly(log N )), in comparison to the best case time complexity for classical algorithms of O(N^3 ). The HHL algorithm outlines a use for quantum circuits outside of quantum applications. One such application is in machine learning, as many networks use linear regression in their training algorithm. Currently it is not feasible to solve for weight vectors of floating point precision on a quantum computer, but if the weight vector is constrained to binary values 0 or 1 then the problem becomes small enough to implement even on current noisy quantum computers. This work outlines two different circuit designs to solve for 2 × 2 and 4 × 4 systems of equations, so long as the matrices follow the eigenvalue constraint of having eigenvalues be powers of 2. In addition, the problem of reading data from the quantum state to classical data is addressed through the use of a swap test between the solution state |x> and an test state |test>. By using a swap test vector of all 1s, it is shown it is possible to find how many ones lay in the solution vector; thus reducing the number of possible solution states without performing quantum tomography. While it is not possible to beat classical algorithms with the noise on current quantum circuits, this work shows it is possible to implement quantum algorithms for non-quantum applications establishing potential for future hybrid approaches."--Abstract.
Author : Maria Schuld
Publisher : Springer Nature
ISBN 13 : 3030830985
Total Pages : 321 pages
Book Rating : 4.0/5 (38 download)
Download or read book Machine Learning with Quantum Computers written by Maria Schuld and published by Springer Nature. This book was released on 2021-10-17 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Maria Schuld
Publisher : Springer
ISBN 13 : 3319964240
Total Pages : 293 pages
Book Rating : 4.3/5 (199 download)
Download or read book Supervised Learning with Quantum Computers written by Maria Schuld and published by Springer. This book was released on 2018-08-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Peter Wittek
Publisher : Academic Press
ISBN 13 : 0128010991
Total Pages : 176 pages
Book Rating : 4.1/5 (28 download)
Download or read book Quantum Machine Learning written by Peter Wittek and published by Academic Press. This book was released on 2014-09-10 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research
Author : Jon Adams
Publisher : Green Mountain Computing
ISBN 13 :
Total Pages : 157 pages
Book Rating : 4./5 ( download)
Download or read book AI Foundations Of Quantum Machine Learning written by Jon Adams and published by Green Mountain Computing. This book was released on with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the cutting-edge intersection of quantum computing and machine learning with "AI Foundations of Quantum Machine Learning." This comprehensive guide invites readers into the exciting world where the realms of artificial intelligence (AI) and quantum mechanics merge, setting the stage for a revolution in AI technologies. With the burgeoning interest in quantum computing's vast potential, this book serves as a beacon, illuminating the intricate concepts and groundbreaking promises of quantum machine learning. Contents Quantum Computing: An Introduction - Begin your journey with a primer on quantum computing, understanding the fundamental quantum mechanics that power advanced data processing. Fundamentals of Machine Learning - Lay the groundwork with an overview of machine learning principles, setting the stage for their quantum leap. Quantum Algorithms for Machine Learning - Discover the transformative potential of quantum algorithms, capable of processing large datasets with unprecedented speed and efficiency. Data Encoding in Quantum Systems - Explore the innovative techniques for encoding data into quantum systems, a crucial step for quantum machine learning. Quantum Machine Learning Models - Delve into the heart of quantum machine learning, examining models that harness quantum mechanics to enhance machine learning capabilities. Training Quantum Neural Networks - Unpack the methodologies for training quantum neural networks, a pioneering approach to AI development. Applications of Quantum Machine Learning - Witness the practical implications of quantum machine learning across various fields, from healthcare to environmental science. Challenges and the Future Landscape - Reflect on the hurdles facing quantum machine learning and envision the future of AI shaped by quantum advancements. Introduction "AI Foundations of Quantum Machine Learning" offers a compelling narrative on the symbiosis of quantum computing and machine learning. Through accessible language and vivid examples, it demystifies complex concepts and showcases the transformative power of quantum technologies in AI. Readers are taken on an enlightening journey, from the basic principles of quantum computing to the forefront of quantum machine learning models and their applications. This book is not merely an academic text; it is a roadmap to the future, encouraging readers to envision a world where AI is redefined by quantum phenomena. Ideal for students, academics, and tech enthusiasts alike, this book bridges the gap between theoretical quantum mechanics and practical machine learning applications. Whether you're looking to understand the basics or explore the future of technology, "AI Foundations of Quantum Machine Learning" is an indispensable resource for anyone eager to grasp the next wave of technological innovation.
Author : Pethuru Raj
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3111342271
Total Pages : 336 pages
Book Rating : 4.1/5 (113 download)
Download or read book Quantum Machine Learning written by Pethuru Raj and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-08-05 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Siddhartha Bhattacharyya
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110670720
Total Pages : 144 pages
Book Rating : 4.1/5 (16 download)
Download or read book Quantum Machine Learning written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-06-08 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.
Author : Romil Rawat
Publisher : John Wiley & Sons
ISBN 13 : 1394167385
Total Pages : 440 pages
Book Rating : 4.3/5 (941 download)
Download or read book Quantum Computing in Cybersecurity written by Romil Rawat and published by John Wiley & Sons. This book was released on 2023-10-19 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, deep learning, probabilistic neural networks, blockchain, and other new technologies all demand extremely high processing speeds. A quantum computer is an example of such a system. Quantum computers may be accessed over the internet. This technology poses a significant risk, since quantum terrorists, or cyber criminals, coul be able to cause many problems, including bringing down the internet. The principles of quantum mechanics might be used by evil doers to destroy quantum information on a global scale, and an entire class of suspicious codes could destroy data or eavesdrop on communication. Quantum physics, however, safeguards against data eavesdropping. A significant amount of money is being invested in developing and testing a quantum version of the internet that will eliminate eavesdropping and make communication nearly impenetrable to cyber-attacks. The simultaneous activation of quantum terrorists (organized crime) can lead to significant danger by attackers introducing quantum information into the network, breaking the global quantum state, and preventing the system from returning to its starting state. Without signs of identifying information and real-time communication data, such vulnerabilities are very hard to discover. Terrorists' synchronized and coordinated acts have an impact on security by sparking a cyber assault in a fraction of a second. The encryption is used by cyber-criminal groups with the genuine, nefarious, and terrible motives of killing innocent people or stealing money. In the hands of criminals and codes, cryptography is a dangerous and formidable weapon. Small amounts of digital information are hidden in a code string that translates into an image on the screen, making it impossible for the human eye to identify a coded picture from its uncoded equivalents. To steal the cryptographic key necessary to read people's credit card data or banking information, cyber thieves employ installed encryption techniques, human mistakes, keyboard loggers, and computer malware. This new volume delves into the latest cutting-edge trends and the most up-to-date processes and applications for quantum computing to bolster cybersecurity. Whether for the veteran computer engineer working in the field, other computer scientists and professionals, or for the student, this is a one-stop-shop for quantum computing in cyber security and a must have for any library.
Author : Stefanie Czischek
Publisher : Springer Nature
ISBN 13 : 3030527158
Total Pages : 205 pages
Book Rating : 4.0/5 (35 download)
Download or read book Neural-Network Simulation of Strongly Correlated Quantum Systems written by Stefanie Czischek and published by Springer Nature. This book was released on 2020-08-27 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.
Author : Kedar Nath Das
Publisher : Springer
ISBN 13 : 8132222202
Total Pages : 657 pages
Book Rating : 4.1/5 (322 download)
Download or read book Proceedings of Fourth International Conference on Soft Computing for Problem Solving written by Kedar Nath Das and published by Springer. This book was released on 2014-12-23 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Proceedings of SocProS 2014 serves as an academic bonanza for scientists and researchers working in the field of Soft Computing. This book contains theoretical as well as practical aspects using fuzzy logic, neural networks, evolutionary algorithms, swarm intelligence algorithms, etc., with many applications under the umbrella of ‘Soft Computing’. The book is beneficial for young as well as experienced researchers dealing across complex and intricate real world problems for which finding a solution by traditional methods is a difficult task. The different application areas covered in the Proceedings are: Image Processing, Cryptanalysis, Industrial Optimization, Supply Chain Management, Newly Proposed Nature Inspired Algorithms, Signal Processing, Problems related to Medical and Healthcare, Networking Optimization Problems, etc.
Author : Melanie Swan
Publisher : World Scientific
ISBN 13 : 1786348225
Total Pages : 400 pages
Book Rating : 4.7/5 (863 download)
Download or read book Quantum Computing: Physics, Blockchains, And Deep Learning Smart Networks written by Melanie Swan and published by World Scientific. This book was released on 2020-03-20 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum information and contemporary smart network domains are so large and complex as to be beyond the reach of current research approaches. Hence, new theories are needed for their understanding and control. Physics is implicated as smart networks are physical systems comprised of particle-many items interacting and reaching criticality and emergence across volumes of macroscopic and microscopic states. Methods are integrated from statistical physics, information theory, and computer science. Statistical neural field theory and the AdS/CFT correspondence are employed to derive a smart network field theory (SNFT) and a smart network quantum field theory (SNQFT) for the orchestration of smart network systems. Specifically, a smart network field theory (conventional or quantum) is a field theory for the organization of particle-many systems from a characterization, control, criticality, and novelty emergence perspective.This book provides insight as to how quantum information science as a paradigm shift in computing may influence other high-impact digital transformation technologies, such as blockchain and machine learning. Smart networks refer to the idea that the internet is no longer simply a communications network, but rather a computing platform. The trajectory is that of communications networks becoming computing networks (with self-executing code), and perhaps ultimately quantum computing networks. Smart network technologies are conceived as autonomous self-operating computing networks. This includes blockchain economies, deep learning neural networks, autonomous supply chains, self-piloting driving fleets, unmanned aerial vehicles, industrial robotics cloudminds, real-time bidding for advertising, high-frequency trading networks, smart city IoT sensors, and the quantum internet.
Author : Claudio Conti
Publisher : Springer Nature
ISBN 13 : 3031442261
Total Pages : 393 pages
Book Rating : 4.0/5 (314 download)
Download or read book Quantum Machine Learning written by Claudio Conti and published by Springer Nature. This book was released on 2024-01-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Frank Zickert
Publisher : Independently Published
ISBN 13 :
Total Pages : 440 pages
Book Rating : 4.5/5 (165 download)
Download or read book Hands-On Quantum Machine Learning With Python written by Frank Zickert and published by Independently Published. This book was released on 2021-06-19 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.
Author : Elias F. Combarro
Publisher : Packt Publishing Ltd
ISBN 13 : 1804618306
Total Pages : 680 pages
Book Rating : 4.8/5 (46 download)
Download or read book A Practical Guide to Quantum Machine Learning and Quantum Optimization written by Elias F. Combarro and published by Packt Publishing Ltd. This book was released on 2023-03-31 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guide Key FeaturesGet a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisitesLearn the process of implementing the algorithms on simulators and actual quantum computersSolve real-world problems using practical examples of methodsBook Description This book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You'll be introduced to quantum computing using a hands-on approach with minimal prerequisites. You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that's ready to be run on quantum simulators and actual quantum computers. You'll also learn how to utilize programming frameworks such as IBM's Qiskit, Xanadu's PennyLane, and D-Wave's Leap. Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away. What you will learnReview the basics of quantum computingGain a solid understanding of modern quantum algorithmsUnderstand how to formulate optimization problems with QUBOSolve optimization problems with quantum annealing, QAOA, GAS, and VQEFind out how to create quantum machine learning modelsExplore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLaneDiscover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interfaceWho this book is for This book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
Author : Davide Pastorello
Publisher : Springer Nature
ISBN 13 : 9811968977
Total Pages : 144 pages
Book Rating : 4.8/5 (119 download)
Download or read book Concise Guide to Quantum Machine Learning written by Davide Pastorello and published by Springer Nature. This book was released on 2022-12-16 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research. To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.
Author : Alexandr A. Ezhov
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.:/5 (318 download)
Download or read book Introduction to Quantum Neural Technologies written by Alexandr A. Ezhov and published by . This book was released on 2003 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a first introductory book in Quantum Neural Technology -- a new and promising area of informatics. Combination of the ideas from quantum computing and neural computing raises the possibility of dramatically decreasing the complexity of neural systems by replacing networks of classical neurons with a single quantum neuron. In the first two chapters, the fundamentals of neural technologies and of quantum computing are presented. In the third chapter, it is outlined how the problems typical for classical neural technology can be solved by using quantum neural technology. This book is very useful to students, teachers, researchers, and engineers, who are working in informatics or just interested in being briefly aware of it.
