A practical guide to quantum machine learning and quantum optimisation : hands-on primer to quantum computing - from qubits to quantum machine learning and beyond

A practical guide to quantum machine learning and quantum optimisation hands-on primer to quantum computing - from qubits to quantum machine learning and beyond

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 th...

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Detalles Bibliográficos
Otros Autores: Combarro, Elias F., author (author), Meglio, Alberto Di, author, Gonzalez-Castillo, Samuel, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : Packt Publishing [2023]
Edición:1st ed
Materias:
Quantum computing.
Machine learning.
Mathematical optimization.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009730939006719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Foreword
  • Acknowledgements
  • Table of Contents
  • Preface
  • Part 1: I, for One, Welcome our New Quantum Overlords
  • Chapter 1: Foundations of Quantum Computing
  • Quantum computing: the big picture
  • The basics of the quantum circuit model
  • Working with one qubit and the Bloch sphere
  • What is a qubit?
  • Dirac notation and inner products
  • One-qubit quantum gates
  • The Bloch sphere and rotations
  • Hello, quantum world!
  • Working with two qubits and entanglement
  • Two-qubit states
  • Two-qubit gates: tensor products
  • The CNOT gate
  • Entanglement
  • The no-cloning theorem
  • Controlled gates
  • Hello, entangled world!
  • Working with multiple qubits and universality
  • Multi-qubit systems
  • Multi-qubit gates
  • Universal gates in quantum computing
  • Summary
  • Chapter 2: The Tools of the Trade in Quantum Computing
  • Tools for quantum computing: a non-exhaustive overview
  • A non-exhaustive survey of frameworks and platforms
  • Qiskit, PennyLane, and Ocean
  • Working with Qiskit
  • An overview of the Qiskit framework
  • Using Qiskit Terra to build quantum circuits
  • Initializing circuits
  • Quantum gates
  • Measurements
  • Using Qiskit Aer to simulate quantum circuits
  • Let's get real: using IBM Quantum
  • Working with PennyLane
  • Circuit engineering 101
  • PennyLane's interoperability
  • Love is in the Aer
  • Connecting to IBMQ
  • Summary
  • Part 2: When Time is Gold: Tools for Quantum Optimization
  • Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems
  • The Max-Cut problem and the Ising model
  • Graphs and cuts
  • Formulating the problem
  • The Ising model
  • Enter quantum: formulating optimization problems the quantum way
  • From classical variables to qubits
  • Computing expectation values with Qiskit
  • Moving from Ising to QUBO and back.
  • Combinatorial optimization problems with the QUBO model
  • Binary linear programming
  • The Knapsack problem
  • Graph coloring
  • The Traveling Salesperson Problem
  • Other problems and other formulations
  • Summary
  • Chapter 4: Adiabatic Quantum Computing and Quantum Annealing
  • Adiabatic quantum computing
  • Quantum annealing
  • Using Ocean to formulate and transform optimization problems
  • Constrained quadratic models in Ocean
  • Solving constrained quadratic models with dimod
  • Running constrained problems on quantum annealers
  • Solving optimization problems on quantum annealers with Leap
  • The Leap annealers
  • Embeddings and annealer topologies
  • Controlling annealing parameters
  • The importance of coupling strengths
  • Classical and hybrid samplers
  • Classical solvers
  • Hybrid solvers
  • Summary
  • Chapter 5: QAOA: Quantum Approximate Optimization Algorithm
  • From adiabatic computing to QAOA
  • Discretizing adiabatic quantum computing
  • QAOA: The algorithm
  • Circuits for QAOA
  • Estimating the energy
  • QUBO and HOBO
  • Using QAOA with Qiskit
  • Using QAOA with Hamiltonians
  • Solving QUBO problems with QAOA in Qiskit
  • Using QAOA with PennyLane
  • Summary
  • Chapter 6: GAS: Grover Adaptive Search
  • Grover's algorithm
  • Quantum oracles
  • Grover's circuits
  • Probability of finding a marked element
  • Finding minima with Grover's algorithm
  • Quantum oracles for combinatorial optimization
  • The quantum Fourier transform
  • Encoding and adding integer numbers
  • Computing the whole polynomial
  • Constructing the oracle
  • Using GAS with Qiskit
  • Summary
  • Chapter 7: VQE: Variational Quantum Eigensolver
  • Hamiltonians, observables, and their expectation values
  • Observables
  • Estimating the expectation values of observables
  • Introducing VQE
  • Getting excited with VQE
  • Using VQE with Qiskit.
  • Defining a molecular problem in Qiskit
  • Using VQE with Hamiltonians
  • Finding excited states with Qiskit
  • Using VQE with molecular problems
  • Simulations with noise
  • Running VQE on quantum computers
  • The shape of things to come: the future of Qiskit
  • Using VQE with PennyLane
  • Defining a molecular problem in PennyLane
  • Implementing and running VQE
  • Running VQE on real quantum devices
  • Summary
  • Part 3: A Match Made in Heaven: Quantum Machine Learning
  • Chapter 8: What Is Quantum Machine Learning?
  • The basics of machine learning
  • The ingredients for machine learning
  • Types of machine learning
  • Do you wanna train a model?
  • Picking a model
  • Understanding loss functions
  • Gradient descent
  • Getting in the (Tensor)Flow
  • Training the model
  • Binary classifier performance
  • Quantum-classical models
  • Summary
  • Chapter 9: Quantum Support Vector Machines
  • Support vector machines
  • The simplest classifier you could think of
  • How to train support vector machines: the hard-margin case
  • Soft-margin training
  • The kernel trick
  • Going quantum
  • The general idea behind quantum support vector machines
  • Feature maps
  • Quantum support vector machines in PennyLane
  • Setting the scene for training a QSVM
  • PennyLane and scikit-learn go on their first date
  • Reducing the dimensionality of a dataset
  • Implementing and using custom feature maps
  • Quantum support vector machines in Qiskit
  • QSVMs on Qiskit Aer
  • QSVMs on IBM quantum computers
  • Summary
  • Chapter 10: Quantum Neural Networks
  • Building and training a quantum neural network
  • A journey from classical neural networks to quantum neural networks
  • Variational forms
  • A word about measurements
  • Gradient computation and the parameter shift rule
  • Practical usage of quantum neural networks
  • Quantum neural networks in PennyLane.
  • Preparing data for a QNN
  • Building the network
  • Using TensorFlow with PennyLane
  • Gradient computation in PennyLane
  • Quantum neural networks in Qiskit: a commentary
  • Summary
  • Chapter 11: The Best of Both Worlds: Hybrid Architectures
  • The what and why of hybrid architectures
  • Hybrid architectures in PennyLane
  • Setting things up
  • A binary classification problem
  • Training models in the real world
  • A multi-class classification problem
  • A general perspective on multi-class classification tasks
  • Implementing a QNN for a ternary classification problem
  • Hybrid architectures in Qiskit
  • Nice to meet you, PyTorch!
  • Setting up a model in PyTorch
  • Training a model in PyTorch
  • Building a hybrid binary classifier with Qiskit
  • Training Qiskit QNNs with Runtime
  • A glimpse into the future
  • Summary
  • Chapter 12: Quantum Generative Adversarial Networks
  • GANs and their quantum counterparts
  • A seemingly unrelated story about money
  • What actually is a GAN?
  • Some technicalities about GANs
  • Quantum GANs
  • Quantum GANs in PennyLane
  • Preparing a QGAN model
  • The training process
  • Quantum GANs in Qiskit
  • Summary
  • Afterword and Appendices
  • Chapter 13: Afterword: The Future of Quantum Computing
  • Appendix A: Complex Numbers
  • Appendix B: Basic Linear Algebra
  • Vector spaces
  • Bases and coordinates
  • Linear maps and eigenstuff
  • Inner products and adjoint operators
  • Matrix exponentiation
  • A crash course in modular arithmetic
  • Appendix C: Computational Complexity
  • A few words on Turing machines
  • Measuring computational time
  • Asymptotic complexity
  • P and NP
  • Hardness, completeness, and reductions
  • A very brief introduction to quantum computational complexity
  • Appendix D: Installing the Tools
  • Getting Python
  • Installing the libraries
  • Accessing IBM's quantum computers.
  • Accessing D-Wave quantum annealers
  • 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 Might Enjoy.

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