Download or read book Quantum Machines written by Michel Devoret and published by Oxford University Press, USA. This book was released on 2014 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is a quantum machine? Can we say that lasers and transistors are quantum machines? After all, physicists advertise these devices as the two main spin-offs of the understanding of quantum physics. In a true quantum machine, the signal collective variables must themselves be treated as quantum operators. Other engineered quantum systems based on natural, rather than artificial, degrees of freedom can also qualify as quantum machines. This book provides the basic knowledge needed to understand and investigate the physics of these novel systems.

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

Download or read book Schrodinger s Machines written by Gerard J. Milburn and published by W. H. Freeman. This book was released on 1997-04-15 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his foreword to Schrödinger's Machines, Paul Davies writes, "The nineteenth century was known as the machine age, the twentieth century will go down in history as the information age. I believe the twenty-first century will be the quantum age." Perhaps the most successful scientific theory in history, quantum mechanics has already ushered in the information age with inventions like the transistor and the laser. In Schrödinger's Machines, renowned quantum physicist Gerard Milburn explores how our ever-increasing ability to manipulate atomic and subatomic processes is turning purely hypothetical situations and concepts (of a truly weird nature) into concrete, practical devices-- resulting in a complete transformation of our world view. Imagine the creation of machines the size of molecules, detectors sensitive enough to pick up the sound of a pin dropping on the other side of the earth, the fabrication of new and exotic materials, and extraordinarily powerful computers that can process information in many alternative realities simultaneously, creating a whole new type of mathematics. This isn't science fiction, but just some of the breathtaking possibilities offered by quantum technology over the next fifty years. Leaving the common sense of Newtonian machines far behind, Schrödinger's Machines is an advance preview of the strange new world ahead. Clearly presented, and with an acute awareness of recent advances in the field, it's indispensable reading for anyone interested in the future.

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.

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.

Download or read book Programming Quantum Computers written by Eric R. Johnston and published by O'Reilly Media. This book was released on 2019-07-03 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum computers are set to kick-start a second computing revolution in an exciting and intriguing way. Learning to program a Quantum Processing Unit (QPU) is not only fun and exciting, but it's a way to get your foot in the door. Like learning any kind of programming, the best way to proceed is by getting your hands dirty and diving into code. This practical book uses publicly available quantum computing engines, clever notation, and a programmer’s mindset to get you started. You'll be able to build up the intuition, skills, and tools needed to start writing quantum programs and solve problems that you care about.

Download or read book Natural Computing DNA Quantum Bits and the Future of Smart Machines written by Dennis E. Shasha and published by W. W. Norton & Company. This book was released on 2010-04-27 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing on interviews with 15 leading scientists, the authors present an unexpected vision: the future of computing is a synthesis with nature.

Download or read book Quantum Computing written by Nihal Mehta Ph.D. and published by Pragmatic Bookshelf. This book was released on 2020-08-26 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: You've heard that quantum computing is going to change the world. Now you can check it out for yourself. Learn how quantum computing works, and write programs that run on the IBM Q quantum computer, one of the world's first functioning quantum computers. Learn a simple way to apply quantum mechanics to computer programming. Create algorithms to solve intractable problems for classical computers, and discover how to explore the entire problem space at once to determine the optimal solution. Get your hands on the future of computing today. Quantum computing overhauls computer science. Problems such as designing life-saving drugs and super-large logistics problems that have been difficult or impossible for classical computers to handle can now be solved in moments. Quantum computing makes it possible to explore all possible solutions simultaneously and determine those that work, instead of iterating through each possibility sequentially. Work with quantum computers directly, instead of talking about them theoretically. Discover a new visual way of looking at quantum bits that makes quantum computing intuitive for computer programmers. Master the special properties that make them different, and more powerful, than classical bits. Control quantum bits with gates and create circuits to model complex problems. Write programs that run on real quantum machines to solve problems that classical computers struggle with. Dive into quantum optimization and cryptography. Get a head start on the technology that will drive computer science into the future. What You Need: Access to the IBM quantum computer, via any internet connection

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.

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.

Download or read book Quantum Machine Learning and Optimisation in Finance written by Antoine Jacquier and published by Packt Publishing Ltd. This book was released on 2022-10-31 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF Key Features Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods Use methods of analogue and digital quantum computing to build powerful generative models Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers Book Description With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun! What you will learn Train parameterised quantum circuits as generative models that excel on NISQ hardware Solve hard optimisation problems Apply quantum boosting to financial applications Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work Analyse the latest algorithms from quantum kernels to quantum semidefinite programming Apply quantum neural networks to credit approvals Who this book is for This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.

Download or read book Quantum Computing Since Democritus written by Scott Aaronson and published by Cambridge University Press. This book was released on 2013-03-14 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: Takes students and researchers on a tour through some of the deepest ideas of maths, computer science and physics.

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.

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 131 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.

Download or read book Quantum Computer Systems written by Yongshan Ding and published by Springer Nature. This book was released on 2022-05-31 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.

Download or read book Quantum Computers Algorithms and Chaos written by Giulio Casati and published by IOS Press. This book was released on 2006 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Information Processing and Communication (QIPC) has the potential to revolutionize many areas of science and technology. This book covers the following topics: introduction to quantum computing; quantum logic, information and entanglement; quantum algorithms; error-correcting codes for quantum computations; quantum communication; and more."

Download or read book Quantum Mechanics And Bayesian Machines written by George Chapline and published by World Scientific. This book was released on 2023-04-14 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.