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.
Author(s): Davide Pastorello
Series: Machine Learning: Foundations, Methodologies, and Applications
Publisher: Springer
Year: 2022
Language: English
Pages: 143
City: Singapore
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
