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Quantum Machine Learning With Python
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Book Synopsis Hands-On Quantum Machine Learning With Python by : Frank Zickert
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.
Book Synopsis Quantum Machine Learning With Python by : Santanu Pattanayak
Download or read book Quantum Machine Learning With Python written by Santanu Pattanayak and published by Apress. This book was released on 2021-03-29 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others. You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others. You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research. What You'll Learn Understand Quantum computing and Quantum machine learning Explore varied domains and the scenarios where Quantum machine learning solutions can be applied Develop expertise in algorithm development in varied Quantum computing frameworks Review the major challenges of building large scale Quantum computers and applying its various techniques Who This Book Is For Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning
Book Synopsis Quantum Machine Learning: An Applied Approach by : Santanu Ganguly
Download or read book Quantum Machine Learning: An Applied Approach written by Santanu Ganguly and published by Apress. This book was released on 2021-08-11 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, and 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
Book Synopsis Learn Quantum Computing with Python and Q# by : Sarah C. Kaiser
Download or read book Learn Quantum Computing with Python and Q# written by Sarah C. Kaiser and published by Simon and Schuster. This book was released on 2021-07-27 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Summary Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you’ll build your own quantum simulator and apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Quantum computers present a radical leap in speed and computing power. Improved scientific simulations and new frontiers in cryptography that are impossible with classical computing may soon be in reach. Microsoft’s Quantum Development Kit and the Q# language give you the tools to experiment with quantum computing without knowing advanced math or theoretical physics. About the book Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Use Python to build your own quantum simulator and take advantage of Microsoft’s open source tools to fine-tune quantum algorithms. The authors explain complex math and theory through stories, visuals, and games. You’ll learn to apply quantum to real-world applications, such as sending secret messages and solving chemistry problems. What's inside The underlying mechanics of quantum computers Simulating qubits in Python Exploring quantum algorithms with Q# Applying quantum computing to chemistry, arithmetic, and data About the reader For software developers. No prior experience with quantum computing required. About the author Dr. Sarah Kaiser works at the Unitary Fund, a non-profit organization supporting the quantum open-source ecosystem, and is an expert in building quantum tech in the lab. Dr. Christopher Granade works in the Quantum Systems group at Microsoft, and is an expert in characterizing quantum devices. Table of Contents PART 1 GETTING STARTED WITH QUANTUM 1 Introducing quantum computing 2 Qubits: The building blocks 3 Sharing secrets with quantum key distribution 4 Nonlocal games: Working with multiple qubits 5 Nonlocal games: Implementing a multi-qubit simulator 6 Teleportation and entanglement: Moving quantum data around PART 2 PROGRAMMING QUANTUM ALGORITHMS IN Q# 7 Changing the odds: An introduction to Q# 8 What is a quantum algorithm? 9 Quantum sensing: It’s not just a phase PART 3 APPLIED QUANTUM COMPUTING 10 Solving chemistry problems with quantum computers 11 Searching with quantum computers 12 Arithmetic with quantum computers
Book Synopsis Learn Quantum Computing with Python and IBM Quantum Experience by : Robert Loredo
Download or read book Learn Quantum Computing with Python and IBM Quantum Experience written by Robert Loredo and published by Packt Publishing Ltd. This book was released on 2020-09-28 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: A step-by-step guide to learning the implementation and associated methodologies in quantum computing with the help of the IBM Quantum Experience, Qiskit, and Python that will have you up and running and productive in no time Key FeaturesDetermine the difference between classical computers and quantum computersUnderstand the quantum computational principles such as superposition and entanglement and how they are leveraged on IBM Quantum Experience systemsRun your own quantum experiments and applications by integrating with QiskitBook Description IBM Quantum Experience is a platform that enables developers to learn the basics of quantum computing by allowing them to run experiments on a quantum computing simulator and a real quantum computer. This book will explain the basic principles of quantum mechanics, the principles involved in quantum computing, and the implementation of quantum algorithms and experiments on IBM's quantum processors. You will start working with simple programs that illustrate quantum computing principles and slowly work your way up to more complex programs and algorithms that leverage quantum computing. As you build on your knowledge, you'll understand the functionality of IBM Quantum Experience and the various resources it offers. Furthermore, you'll not only learn the differences between the various quantum computers but also the various simulators available. Later, you'll explore the basics of quantum computing, quantum volume, and a few basic algorithms, all while optimally using the resources available on IBM Quantum Experience. By the end of this book, you'll learn how to build quantum programs on your own and have gained practical quantum computing skills that you can apply to your business. What you will learnExplore quantum computational principles such as superposition and quantum entanglementBecome familiar with the contents and layout of the IBM Quantum ExperienceUnderstand quantum gates and how they operate on qubitsDiscover the quantum information science kit and its elements such as Terra and AerGet to grips with quantum algorithms such as Bell State, Deutsch-Jozsa, Grover's algorithm, and Shor's algorithmHow to create and visualize a quantum circuitWho this book is for This book is for Python developers who are looking to learn quantum computing and put their knowledge to use in practical situations with the help of IBM Quantum Experience. Some background in computer science and high-school-level physics and math is required.
Book Synopsis Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits by : Tarek Amr
Download or read book Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits written by Tarek Amr and published by Packt Publishing Ltd. This book was released on 2020-07-24 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm’s hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Book Synopsis Quantum Machine Learning by : Peter Wittek
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
Book Synopsis Supervised Learning with Quantum Computers by : Maria Schuld
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.
Book Synopsis Machine Learning Meets Quantum Physics by : Kristof T. Schütt
Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Hands-On Quantum Machine Learning With Python by : Frank Zickert
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 2023-01-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to become a quantum machine learning practitioner? ... But you don't want to study theoretical physics first Then, "Hands-On Quantum Machine Learning With Python" is for you. This book has one goal - to help developers, practitioners, and students like yourself become quantum machine learning experts. It doesn't matter if it is the first time you have worked with machine learning and quantum computing. Hands-On Quantum Machine Learning With Python is engineered from the ground up to help you reach expert status. Inside this book, you'll find: Super practical walkthroughs present solutions to real-world combinatorial optimization problems and challenges. Hands-on tutorials (with lots of code) show you the Variational Quantum Eigensolver and its implementation and usage. An accessible teaching style guaranteed to get you through the underlying maths and physics and master machine quantum learning. In this volume, you will learn how to solve current optimization problems on real quantum computers. We will dive deep into the Variational Quantum Eigensolver (VQE) and use it to solve combinatorial optimization problems. Combinatorial optimization is of paramount importance in many industries. For example, the famous Traveling Salesman Problem (TSP) asks for the shortest route between different cities. It is crucial for parcel delivery, aviation, and almost all mobility-related fields. The ability to solve these problems will enable you to be well prepared to find or keep a job in any of these fields being disrupted by the advent of quantum computing. -- Is this book right for me? -- You don't need to be a mathematician. You don't need to be a physicist, either. This book is for students, developers, data scientists, and practitioners interested in applying quantum machine learning to actual problems - today. "I am new to quantum computing and machine learning altogether." - No problem! Hands-On Quantum Machine Learning With Python is precisely what you need. We start with the absolute basics. We assume no prior knowledge of machine learning or quantum computing. You will not be left behind. (Please claim a bundle including "Volume 1: Getting Started"). "I have a computer science or programming background. Will I understand quantum machine learning?" - Absolutely! This book explains quantum machine learning in an accessible way, even if you are not a mathematician or a physicist. You'll find many code examples and explanations in no other book! "I'm an experienced data scientist or machine learning engineer." - The problems we solve will be familiar to you, but how we solve them will be new. The quantum algorithms we use will become an entirely new tool in your toolbox that you may not have even known existed. And yet, it's the tool you need to master if you want to keep your job in the future. "I am an expert in my field. But I don't have a Ph.D. What are my chances of becoming an expert in quantum computing?" Employers are looking for a rare mix of skills. On the one hand, they look for candidates who are experts in their field. On the other hand, they are looking for candidates with a well-equipped toolbox for machine learning with quantum computing. You are in pole position! -- What's inside this book? -- Hands-On Quantum Machine Learning With Python will make you an expert in solving combinatorial optimization problems with a quantum computer. Inside the book, we will focus on the following: Combinatorial optimization The Variational Quantum Eigensolver (VQE) Problem formulation Various solution ansatzes Running algorithms on real quantum computers Quantum error mitigation The Quantum Approximate Optimization Algorithm
Book Synopsis Quantum Machine Learning with Python by : Santanu Pattanayak
Download or read book Quantum Machine Learning with Python written by Santanu Pattanayak and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others. You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others. You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research. You will: Understand Quantum computing and Quantum machine learning Explore varied domains and the scenarios where Quantum machine learning solutions can be applied Develop expertise in algorithm development in varied Quantum computing frameworks Review the major challenges of building large scale Quantum computers and applying its various techniques.
Book Synopsis Quantum Computing with Python by : Jason Test
Download or read book Quantum Computing with Python written by Jason Test and published by Independently Published. This book was released on 2021-03-17 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: *KINDLE VERSION Discounted at $ 9.99 instead of $ 14.99... Get QUANTUM PHYSICS section for FREE!! "Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning" Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your business thanks to the web applications? Finally on launch the most complete Python+Quantum Physics guide with 4 Manuscripts in 1 book! This is a challenging tool to find real help with many unique contents that indirectly will answer to your doubts: 1-Python for beginners 2-Python for Data Science 3-Python Crash Course and special and FREE section: 4-Quantum Physics for beginners QUANTUM COMPUTING WITH PYTHON will introduce you many selected practices for coding. You will discover as a beginner the world of data science, machine learning and artificial intelligence. The following list is just a tiny fraction of what you will learn in this collection bundle. 1) Python for beginners ✓ The basics of Python programming ✓ Easy-to-follow steps for reading and writing codes. ✓ 3 best strategies with NumPy, Pandas, Matplotlib 2) Python for Data science ✓3 reasons why Python is fundamental for Data Science ✓How to use Python Data Analysis in your business ✓ How to set up the Python environment for Data Science ✓Most important Machine Learning Algorithms 3) Python Crash Course ✓ A Proven Method to Write your First Program in 7 Days ✓The One Thing You Need to Debug your Codes in Python ✓5 Practical exercises to start programming 4) Quantum Physics for beginners ✓The law and principles of quantum physics and the law of attraction; ✓The power of quantum ✓Differences between Quantum cryptography and Quantum computers Examples and step-by-step guides will guide you during the code-writing learning process. The description of each topic is crystal-clear and you can easily practice with related exercises. You will also learn all the 3 best tricks of writing codes with point by point descriptions of the code elements. Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. If you really wish to to learn Python and master its language, please click the BUY NOW button.
Book Synopsis Quantum State Estimation by : Matteo Paris
Download or read book Quantum State Estimation written by Matteo Paris and published by Springer Science & Business Media. This book was released on 2004-08-11 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive survey of most of the theoretical and experimental achievements in the field of quantum estimation of states and operations. Albeit still quite young, this field has already been recognized as a necessary tool for research in quantum optics and quantum information, beyond being a fascinating subject in its own right since it touches upon the conceptual foundations of quantum mechanics. The book consists of twelve extensive lectures that are essentially self-contained and modular, allowing combination of various chapters as a basis for advanced courses and seminars on theoretical or experimental aspects. The last two chapters, for instance, form a self-contained exposition on quantum discrimination problems. The book will benefit graduate students and newcomers to the field as a high-level but accessible textbook, lecturers in search for advanced course material and researchers wishing to consult a modern and authoritative source of reference.
Book Synopsis Programming Quantum Computers by : Eric R. Johnston
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.
Book Synopsis Quantum Computing in Practice with Qiskit® and IBM Quantum Experience® by : Hassi Norlén
Download or read book Quantum Computing in Practice with Qiskit® and IBM Quantum Experience® written by Hassi Norlén and published by Packt Publishing Ltd. This book was released on 2020-11-23 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the nuances of programming traditional quantum computers and solve the challenges of the future while building and executing quantum programs on IBM Quantum hardware and simulators Key FeaturesWork your way up from writing a simple quantum program to programming complex quantum algorithmsExplore the probabilistic nature of qubits by performing quantum coin tosses and using random number generatorsDelve into quantum algorithms and their practical applications in various domainsBook Description IBM Quantum Experience® is a leading platform for programming quantum computers and implementing quantum solutions directly on the cloud. This book will help you get up to speed with programming quantum computers and provide solutions to the most common problems and challenges. You’ll start with a high-level overview of IBM Quantum Experience® and Qiskit®, where you will perform the installation while writing some basic quantum programs. This introduction puts less emphasis on the theoretical framework and more emphasis on recent developments such as Shor’s algorithm and Grover’s algorithm. Next, you’ll delve into Qiskit®, a quantum information science toolkit, and its constituent packages such as Terra, Aer, Ignis, and Aqua. You’ll cover these packages in detail, exploring their benefits and use cases. Later, you’ll discover various quantum gates that Qiskit® offers and even deconstruct a quantum program with their help, before going on to compare Noisy Intermediate-Scale Quantum (NISQ) and Universal Fault-Tolerant quantum computing using simulators and actual hardware. Finally, you’ll explore quantum algorithms and understand how they differ from classical algorithms, along with learning how to use pre-packaged algorithms in Qiskit® Aqua. By the end of this quantum computing book, you’ll be able to build and execute your own quantum programs using IBM Quantum Experience® and Qiskit® with Python. What you will learnVisualize a qubit in Python and understand the concept of superpositionInstall a local Qiskit® simulator and connect to actual quantum hardwareCompose quantum programs at the level of circuits using Qiskit® TerraCompare and contrast Noisy Intermediate-Scale Quantum computing (NISQ) and Universal Fault-Tolerant quantum computing using simulators and IBM Quantum® hardwareMitigate noise in quantum circuits and systems using Qiskit® IgnisUnderstand the difference between classical and quantum algorithms by implementing Grover’s algorithm in Qiskit®Who this book is for This book is for developers, data scientists, machine learning researchers, or quantum computing enthusiasts who want to understand how to use IBM Quantum Experience® and Qiskit® to implement quantum solutions and gain practical quantum computing experience. Python programming experience is a must to grasp the concepts covered in the book more effectively. Basic knowledge of quantum computing will also be beneficial.
Book Synopsis Quantum Computing: An Applied Approach by : Jack D. Hidary
Download or read book Quantum Computing: An Applied Approach written by Jack D. Hidary and published by Springer Nature. This book was released on 2021-09-29 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these elements together in an updated manner. This work is suitable for both academic coursework and corporate technical training. The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more. This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition. This volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional resources include a table of operators and circuit elements and a companion GitHub site providing code and updates. Jack D. Hidary is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X.
Book Synopsis Dancing with Python by : Robert S. Sutor
Download or read book Dancing with Python written by Robert S. Sutor and published by . This book was released on 2021-08-31 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop skills in Python by implementing exciting algorithms, including mathematical functions, classical searching, data analysis, plotting data, machine learning techniques, and quantum circuits Key Features: Learn Python basics to write elegant and efficient code Create quantum circuits and algorithms using Qiskit and run them on quantum computing hardware and simulators Delve into Python's advanced features, including machine learning, analyzing data, and searching Book Description: Coding is the art and engineering of creating software, and Python has been one of the core coding languages for many years. This introductory Python book helps you learn classical and quantum computing in a unified and practical way. It will help you explore work with numbers, strings, collections, iterators, and files. The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends classical techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in classical coding and programming the latest and most powerful quantum computers. What You Will Learn: Create Python code using numbers, strings, collections, classes, objects, functions, conditionals, loops, and operators Write succinct code the Pythonic way using magic methods, iterators, and generators Explore different quantum gates and use them to build quantum circuits Analyze data, build basic machine learning models and plot the results Search for information using traditional methods and the quantum Grover Search Algorithm Optimize and test your code to run efficiently Who this book is for: The book is for Python and coding beginners. Basic familiarity with algebra, geometry, trigonometry, and logarithms is required as the book does not cover the detailed mathematics and theory of quantum computing. You can check out the author's Dancing with Qubits book, also published by Packt, for an approachable and comprehensive introduction to quantum computing.
