Concise Guide to Quantum Machine Learning

Description

This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research. To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML. Preface Contents Chapter 1 Introduction 1.1 Machine learning generalities 1.2 Machine learning with quantum computers 1.3 Structure of the book Chapter 2 Basics of Quantum Mechanics 2.1 Phenomenology 2.2 Mathematical framework 2.3 Quantum states and observables 2.4 Quantum dynamics Chapter 3 Basics of Quantum Computing 3.1 Encoding data into quantum states 3.2 Quantum circuits 3.3 Quantum oracles 3.4 Adiabatic quantum computing 3.5 Quantum annealing Chapter 4 Relevant quantum algorithms 4.1 Quantum Fourier transform 4.2 Grover's search algorithm 4.3 Amplitude amplification 4.4 Quantum phase estimation Chapter 5 QML toolkit 5.1 QRAM 5.2 Hamiltonian simulation 5.3 SWAP test 5.4 Qdist routine Chapter 6 Quantum clustering 6.1 Quantum principal component analysis 6.2 Quantum K-means 6.3 Quantum K-medians 6.4 Quantum divisive clustering 6.5 Clustering with a quantum annealer Chapter 7 Quantum classification 7.1 Distance-based quantum classification 7.2 Quantum k-nearest neighbors 7.3 Quantum support vector machine 7.4 SVM training with a quantum annealer 7.5 Quantum-inspired classification Chapter 8 Quantum pattern recognition 8.1 Quantum associative memory 8.2 Pattern recognition with quantum Fourier transform 8.3 Adiabatic pattern recognition Chapter 9 Quantum neural networks 9.1 Quantum perceptron 9.2 Quantum feedforward neural networks 9.3 Quantum autoencoder for data compression 9.4 Quantum Boltzmann machine 9.5 Quantum convolutional neural networks 9.6 Quantum generative adversarial networks Chapter 10 Concluding remarks Bibliography