A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms
- Length: 680 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-03-31
- ISBN-10: 1804613835
- ISBN-13: 9781804613832
- Sales Rank: #1216096 (See Top 100 Books)
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 Features
- Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites
- Learn the process of implementing the algorithms on simulators and actual quantum computers
- Solve real-world problems using practical examples of methods
Book 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 learn
- Review the basics of quantum computing
- Gain a solid understanding of modern quantum algorithms
- Understand how to formulate optimization problems with QUBO
- Solve optimization problems with quantum annealing, QAOA, GAS, and VQE
- Find out how to create quantum machine learning models
- Explore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLane
- Discover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interface
Who 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.
Hands-on Approach to Modern Quantum Algorithms Contributors About the authors About the reviewers Foreword Acknowledgements Table of Contents Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Share your thoughts Download a free PDF copy of this book Part I: I, for One, Welcome our New Quantum Overlords Chapter 1: Foundations of Quantum Computing 1.1 Quantum computing: the big picture 1.2 The basics of the quantum circuit model 1.3 Working with one qubit and the Bloch sphere 1.4 Working with two qubits and entanglement 1.5 Working with multiple qubits and universality Summary Chapter 2: The Tools of the Trade in Quantum Computing 2.1 Tools for quantum computing: a non-exhaustive overview 2.2 Working with Qiskit 2.3 Working with PennyLane Summary Part II: When Time is Gold: Tools for Quantum Optimization Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems 3.1 The Max-Cut problem and the Ising model 3.2 Enter quantum: formulating optimization problems the quantum way 3.3 Moving from Ising to QUBO and back 3.4 Combinatorial optimization problems with the QUBO model Summary Chapter 4: Adiabatic Quantum Computing and Quantum Annealing 4.1 Adiabatic quantum computing 4.2 Quantum annealing 4.3 Using Ocean to formulate and transform optimization problems 4.4 Solving optimization problems on quantum annealers with Leap Summary Chapter 5: QAOA: Quantum Approximate Optimization Algorithm 5.1 From adiabatic computing to QAOA 5.2 Using QAOA with Qiskit 5.3 Using QAOA with PennyLane Summary Chapter 6: GAS: Grover Adaptive Search 6.1 Grover’s algorithm 6.2 Quantum oracles for combinatorial optimization 6.3 Using GAS with Qiskit Summary Chapter 7: VQE: Variational Quantum Eigensolver 7.1 Hamiltonians, observables, and their expectation values 7.2 Introducing VQE 7.3 Using VQE with Qiskit 7.4 Using VQE with PennyLane Summary Part III: A Match Made in Heaven: Quantum Machine Learning Chapter 8: What Is Quantum Machine Learning? 8.1 The basics of machine learning 8.2 Do you wanna train a model? 8.3 Quantum-classical models Summary Chapter 9: Quantum Support Vector Machines 9.1 Support vector machines 9.2 Going quantum 9.3 Quantum support vector machines in PennyLane 9.4 Quantum support vector machines in Qiskit Summary Chapter 10: Quantum Neural Networks 10.1 Building and training a quantum neural network 10.2 Quantum neural networks in PennyLane 10.3 Quantum neural networks in Qiskit: a commentary Summary Chapter 11: The Best of Both Worlds: Hybrid Architectures 11.1 The what and why of hybrid architectures 11.2 Hybrid architectures in PennyLane 11.3 Hybrid architectures in Qiskit Summary Chapter 12: Quantum Generative Adversarial Networks 12.1 GANs and their quantum counterparts 12.2 Quantum GANs in PennyLane 12.3 Quantum GANs in Qiskit Summary Part IV: Afterword and Appendices Chapter 13: Afterword: The Future of Quantum Computing Appendix A: Complex Numbers Appendix B: Basic Linear Algebra B.1 Vector spaces B.2 Bases and coordinates B.3 Linear maps and eigenstuff B.4 Inner products and adjoint operators B.5 Matrix exponentiation B.6 A crash course in modular arithmetic Appendix C: Computational Complexity C.1 A few words on Turing machines C.2 Measuring computational time C.3 Asymptotic complexity C.4 P and NP C.5 Hardness, completeness, and reductions C.6 A very brief introduction to quantum computational complexity Appendix D: Installing the Tools D.1 Getting Python D.2 Installing the libraries D.3 Accessing IBM’s quantum computers D.4 Accessing D-Wave quantum annealers D.5 Using GPUs to accelerate simulations in Google Colab Appendix E: Production Notes Assessments Chapter 1, Foundations of Quantum Computing Chapter 2, The Tools of the Trade in Quantum Computing Chapter 3, Working with Quadratic Unconstrained Binary Optimization Problems Chapter 4, Adiabatic Quantum Computing and Quantum Annealing Chapter 5, QAOA: Quantum Approximate Optimization Algorithm Chapter 6, GAS: Grover Adaptative Search Chapter 7, VQE: Variational Quantum Eigensolver Chapter 8, What is Quantum machine Learning? Chapter 9, Quantum Support Vector Machines Chapter 10, Quantum Neural Networks Chapter 11, The Best of Both Worlds: Hybrid Architectures Chapter 12, Quantum Generative Adversarial Networks Bibliography Index Other Books You May Enjoy Why subscribe? Packt is searching for authors like you Share your thoughts Download a free PDF copy of this book
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