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Quantum Machine Learning

Quantum Machine Learning Book
Author : Siddhartha Bhattacharyya,Indrajit Pan,Ashish Mani,Sourav De,Elizabeth Behrman,Susanta Chakraborti
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2020-06-08
ISBN : 3110670704
Language : En, Es, Fr & De

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Book Description :

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.

Quantum Machine Learning

Quantum Machine Learning Book
Author : Peter Wittek
Publisher : Academic Press
Release : 2014-09-10
ISBN : 0128010991
Language : En, Es, Fr & De

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Book Description :

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

Quantum Machine Learning An Applied Approach

Quantum Machine Learning  An Applied Approach Book
Author : Santanu Ganguly
Publisher : Apress
Release : 2021-07-09
ISBN : 9781484270974
Language : En, Es, Fr & De

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Book Description :

Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. What You will Learn Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive Who This Book Is For Data scientists, machine learning professionals, and researchers

Quantum Machine Learning

Quantum Machine Learning Book
Author : Jordi Riu I Vicente
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs.

Quantum Machine Learning for Supervised Pattern Recognition

Quantum Machine Learning for Supervised Pattern Recognition Book
Author : Maria Schuld
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Quantum Machine Learning for Supervised Pattern Recognition book written by Maria Schuld, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Supervised Learning with Quantum Computers

Supervised Learning with Quantum Computers Book
Author : Maria Schuld,Francesco Petruccione
Publisher : Springer
Release : 2018-08-30
ISBN : 3319964240
Language : En, Es, Fr & De

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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.

Quantum Machine Learning in Chemical Space

Quantum Machine Learning in Chemical Space Book
Author : Felix Andreas Faber
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Quantum Machine Learning in Chemical Space book written by Felix Andreas Faber, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Machine Learning Meets Quantum Physics

Machine Learning Meets Quantum Physics Book
Author : Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller
Publisher : Springer Nature
Release : 2020-06-03
ISBN : 3030402452
Language : En, Es, Fr & De

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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.

Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition

Principles Of Quantum Artificial Intelligence  Quantum Problem Solving And Machine Learning  Second Edition  Book
Author : Andreas Miroslaus Wichert
Publisher : World Scientific
Release : 2020-07-08
ISBN : 9811224323
Language : En, Es, Fr & De

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Book Description :

This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.

Limitations and Future Applications of Quantum Cryptography

Limitations and Future Applications of Quantum Cryptography Book
Author : Neeraj Kumar,Alka Agrawal,Brijesh Kumar Chaurasia,Raees Ahmad Khan
Publisher : Information Science Reference
Release : 2020-12-18
ISBN : 1799866793
Language : En, Es, Fr & De

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Book Description :

"This book is for security experts as well as for IoT developers to help them understand the concepts related to quantum cryptography and classical cryptography and providing a direction to security professionals and IoT solution developers toward using approaches of Quantum Cryptography as available computational power increases"--

Hands On Quantum Information Processing with Python

Hands On Quantum Information Processing with Python Book
Author : Dr. Makhamisa Senekane
Publisher : Packt Publishing Ltd
Release : 2021-01-29
ISBN : 1800205724
Language : En, Es, Fr & De

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Book Description :

Quantum computers have the potential to efficiently solve problems that are otherwise unmanageable for classical computers. This book takes a hands-on approach to help you explore the foundation of quantum information processing as well as associated methodologies and implementations to enable you to be productive in no time.

Quantum Algorithms for Linear Algebra and Machine Learning

Quantum Algorithms for Linear Algebra and Machine Learning Book
Author : Anupam Prakash
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Most quantum algorithms offering speedups over classical algorithms are based on the three techniques of phase estimation, amplitude estimation and Hamiltonian simulation. In spite of the linear algebraic nature of the postulates of quantum mechanics, until recent work by Lloyd and coauthors cite{LMR13, LMR13a, LMR13b} no quantum algorithms achieving speedups for linear algebra or machine learning had been proposed. A quantum machine learning algorithm must address three issues: encoding of classical data into a succinct quantum representation, processing the quantum representation and extraction of classically useful information from the processed quantum state. In this dissertation, we make progress on all three aspects of the quantum machine learning problem and obtain quantum algorithms for low rank approximation and regularized least squares. The oracle $QRAM$, the standard model studied in quantum query complexity, requires time $O(sqrt{n})$ to encode vectors $v in R^{n}$ into quantum states. We propose simple hardware augmentations to the oracle $QRAM$, that enable vectors $v in R^{n}$ to be encoded in time $O(log n)$, with pre-processing. The augmented $QRAM$ incurs minimal hardware overheads, the pre-processing can be parallelized and is a flexible model that allows storage of multiple vectors and matrices. It provides a framework for designing quantum algorithms for linear algebra and machine learning. Using the augmented $QRAM$ for vector state preparation, we present two different algorithms for singular value estimation where given singular vector $ket{v}$ for $A in R^{mtimes n}$, the singular value $sigma_{i}$ is estimated within additive error $epsilon norm{A}_{F}$. The first algorithm requires time $wt{1/epsilon^{3}}$ and uses the approach for simulating $e^{-i rho}$ in cite{LMR13}. However, the analysis cite{LMR13} does not establish the coherence of outputs, we provide a qualitatively different analysis that uses the quantum Zeno effect to establish coherence and reveals the probabilistic nature of the simulation technique. The second algorithm has a running time $wt{1/epsilon}$ and uses Jordan's lemma from linear algebra and the augmented $QRAM$ to implement reflections. We use quantum singular value estimation to obtain algorithms for low rank approximation by column selection, the algorithms are based on importance sampling from the leverage score distribution. We obtain quadratic speedups for a large class of linear algebra algorithms that rely on importance sampling from the leverage score distribution including approximate least squares and $CX$ and $CUR$ decompositions. Classical algorithms for these problems require time $O(mn log n + poly(1/epsilon))$, the quantum algorithms have running time $O(sqrt{m}poly(1/epsilon, k, Delta))$ where $k, Delta$ are the rank and spectral gap. The running time of the quantum $CX$ decomposition algorithm does not depend on $m$, it is polynomial in problem parameters. We also provide quantum algorithms for $ell_{2}$ regularized regression problems, the quantum ridge regression algorithm requires time $wt{1/mu^{2} delta}$ to output a quantum state that is $delta$ close to the solution, where $mu$ is the regularization parameter.

QUANTUM MECHANICS AND MACHINE LEARNING

QUANTUM MECHANICS AND MACHINE LEARNING  Book
Author : GEORGE. CHAPLINE
Publisher : Unknown
Release : 2019
ISBN : 9789813232464
Language : En, Es, Fr & De

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Book Description :

Download QUANTUM MECHANICS AND MACHINE LEARNING book written by GEORGE. CHAPLINE, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Blockchain Physics Quantum Computing Distributed Ledgers Machine Learning and Other Smart Network Technologies

Blockchain Physics  Quantum Computing  Distributed Ledgers  Machine Learning  and Other Smart Network Technologies Book
Author : Melanie Swan,Frank Witte,Renato P. Dos Santos
Publisher : World Scientific Publishing Europe Limited
Release : 2020
ISBN : 9781786348203
Language : En, Es, Fr & De

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Book Description :

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.

Quantum Computing and Supervised Machine Learning

Quantum Computing and Supervised Machine Learning Book
Author : Philips Coleman Ph D
Publisher : Unknown
Release : 2021-03-05
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Quantum Cоmрutіng іѕ a new аnd еxсіtіng fіеld аt thе intersection оf mаthеmаtісѕ, computer ѕсіеnсе аnd physics. It соnсеrnѕ a utilization оf quаntum mесhаnісѕ tо іmрrоvе the еffісіеnсу of computation. Hеrе wе present a gеntlе introduction tо ѕоmе оf thе ideas in quаntum computing. The paper begins by motivating thе сеntrаl іdеаѕ of quantum mесhаnісѕ аnd quаntum соmрutаtіоn wіth ѕіmрlе tоу mоdеlѕ. Frоm there wе move on tо a formal presentation оf the ѕmаll frасtіоn of (fіnіtе dіmеnѕіоnаl) quаntum mесhаnісѕ that wе will nееd fоr bаѕіс quantum соmрutаtіоn. Cеntrаl nоtіоnѕ оf quantum аrсhіtесturе (qubіtѕ and quаntum gаtеѕ) are dеѕсrіbеd. There аrе problems thаt еvеn the mоѕt powerful сlаѕѕісаl соmрutеrѕ аrе unable tо ѕоlvе bесаuѕе of thеіr ѕсаlе оr соmрlеxіtу. Quаntum соmрutеrѕ may bе unіquеlу ѕuіtеd tо ѕоlvе ѕоmе оf thеѕе рrоblеmѕ because оf their іnhеrеntlу quаntum properties.

Machine Learning for Decision Makers

Machine Learning for Decision Makers Book
Author : Patanjali Kashyap
Publisher : Apress
Release : 2018-01-04
ISBN : 1484229886
Language : En, Es, Fr & De

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Book Description :

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Quantum classical Generative Models for Machine Learning

Quantum classical Generative Models for Machine Learning Book
Author : Marcello Benedetti
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

The combination of quantum and classical computational resources towards more effective algorithms is one of the most promising research directions in computer science. In such a hybrid framework, existing quantum computers can be used to their fullest extent and for practical applications. Generative modeling is one of the applications that could benefit the most, either by speeding up the underlying sampling methods or by unlocking more general models. In this work, we design a number of hybrid generative models and validate them on real hardware and datasets. The quantum-assisted Boltzmann machine is trained to generate realistic artificial images on quantum annealers. Several challenges in state-of-the-art annealers shall be overcome before one can assess their actual performance. We attack some of the most pressing challenges such as the sparse qubit-to-qubit connectivity, the unknown effective-temperature, and the noise on the control parameters. In order to handle datasets of realistic size and complexity, we include latent variables and obtain a more general model called the quantum-assisted Helmholtz machine. In the context of gate-based computers, the quantum circuit Born machine is trained to encode a target probability distribution in the wavefunction of a set of qubits. We implement this model on a trapped ion computer using low-depth circuits and native gates. We use the generative modeling performance on the canonical Bars-and-Stripes dataset to design a benchmark for hybrid systems. It is reasonable to expect that quantum data, i.e., datasets of wavefunctions, will become available in the future. We derive a quantum generative adversarial network that works with quantum data. Here, two circuits are optimized in tandem: one tries to generate suitable quantum states, the other tries to distinguish between target and generated states.

Quantum Algorithms for Matrix Problems and Machine Learning

Quantum Algorithms for Matrix Problems and Machine Learning Book
Author : Sathyawageeswar Subramanian
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Quantum Algorithms for Matrix Problems and Machine Learning book written by Sathyawageeswar Subramanian, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Quantum Artificial Intelligence

Quantum Artificial Intelligence Book
Author : Bobak Toussi Kiani
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Linear algebra is a simple yet elegant mathematical framework that serves as the mathematical bedrock for many scientific and engineering disciplines. Broadly defined as the study of linear equations represented as vectors and matrices, linear algebra provides a mathematical toolbox for manipulating and controlling many physical systems. For example, linear algebra is central to the modeling of quantum mechanical phenomena and machine learning algorithms. Within the broad landscape of matrices studied in linear algebra, unitary matrices stand apart for their special properties, namely that they preserve norms and have easy to calculate inverses. Interpreted from an algorithmic or control setting, unitary matrices are used to describe and manipulate many physical systems. Relevant to the current work, unitary matrices are commonly studied in quantum mechanics where they formulate the time evolution of quantum states and in artificial intelligence where they provide a means to construct stable learning algorithms by preserving norms. One natural question that arises when studying unitary matrices is how difficult it is to learn them. Such a question may arise, for example, when one would like to learn the dynamics of a quantum system or apply unitary transformations to data embedded into a machine learning algorithm. In this thesis, I examine the hardness of learning unitary matrices both in the context of deep learning and quantum computation. This work aims to both advance our general mathematical understanding of unitary matrices and provide a framework for integrating unitary matrices into classical or quantum algorithms. Different forms of parameterizing unitary matrices, both in the quantum and classical regimes, are compared in this work. In general, experiments show that learning an arbitrary d × d2 unitary matrix requires at least d2 parameters in the learning algorithm regardless of the parameterization considered. In classical (non-quantum) settings, unitary matrices can be constructed by composing products of operators that act on smaller subspaces of the unitary manifold. In the quantum setting, there also exists the possibility of parameterizing unitary matrices in the Hamiltonian setting, where it is shown that repeatedly applying two alternating Hamiltonians is sufficient to learn an arbitrary unitary matrix. Finally, I discuss applications of this work in quantum and deep learning settings. For near term quantum computers, applying a desired set of gates may not be efficiently possible. Instead, desired unitary matrices can be learned from a given set of available gates (similar to ideas discussed in quantum controls). Understanding the learnability of unitary matrices can also aid efforts to integrate unitary matrices into neural networks and quantum deep learning algorithms. For example, deep learning algorithms implemented in quantum computers may leverage parameterizations discussed here to form layers in a quantum learning architecture.

Quantum enhanced Machine Learning in the NISQ Era

Quantum enhanced Machine Learning in the NISQ Era Book
Author : Marco Radic
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Quantum enhanced Machine Learning in the NISQ Era book written by Marco Radic, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.