Quantum Machine Learning: What Quantum Computing Means to Data Mining

cover Machine learning has become indispensable in discovering patterns in large data sets, and the theory is at the core of a larger set of tools known as data mining. It is a mature field with an astonishing array of practical applications.

Quantum computing has the potential of taking machine learning to the next level. While hardware implementations of quantum computing systems are still in an initial phase, recent theoretical developments hint at the benefits of applying quantum methods to learning algorithms.

Computational complexity can be reduced exponentially in some cases, whereas we see quadratic reduction in others. Yet, improved learning time is just one part of the equation. Through nonconvex objective functions, quantum machine learning algorithms are more robust to noise and outliers, which makes their generalization performance better than many known classical algorithms. Examples include quantum support vector machines, learning a function by quantum process tomography, quantum neural networks, and adiabatic quantum optimization.

Quantum Machine Learning: What Quantum Computing Means to Data Mining explains the most relevant concepts of machine learning, quantum mechanics, and quantum information theory, and contrasts classical learning algorithms to their quantum counterparts.

The book is available at Elsevier Store, at Amazon, and also at Barnes and Noble. Elsevier Store gives a 25 % discount with the promotional code PRT2514.

Table of Contents

Part I: Fundamental Concepts

1 Introduction
1.1 Learning Theory and Data Mining
1.2 Why Quantum Computers
1.3 A Heterogeneous Model
1.4 An Overview of Quantum Machine Learning
1.5 Quantum-Like Learning on Classical Computers
2 Machine Learning
2.1 Data-Driven Models
2.2 Feature Space
2.3 Supervised and Unsupervised Learning
2.4 Generalization Performance
2.5 Model Complexity
2.6 Ensembles
2.7 Data Dependencies and Computational Complexity
3 Quantum Mechanics
3.1 States and Superposition
3.2 Density Matrix Representation and Mixed States
3.3 Composite Systems and Entanglement
3.4 Evolution
3.5 Measurement
3.6 Uncertainty Relations
3.7 Tunneling
3.8 Adiabatic Theorem
3.9 No-Cloning Theorem
4 Quantum Computing
4.1 Qubits and the Bloch Sphere
4.2 Quantum Circuits
4.3 Adiabatic Quantum Computing
4.4 Quantum Parallelism
4.5 Grover's Algorithm
4.6 Complexity Classes
4.7 Quantum Information Theory

Part II: Classical Learning Algorithms

5 Unsupervised Learning
5.1 Principal Component Analysis
5.2 Manifold Embedding
5.3 K-Means and K-Medians Clustering
5.4 Hierarchical Clustering
5.5 Density-Based Clustering
6 Pattern Recognition and Neural Networks
6.1 The Perceptron
6.2 Hopfield Networks
6.3 Feedforward Networks
6.4 Deep Learning
6.5 Computational Complexity
7 Supervised Learning and Support Vector Machines
7.1 K-Nearest Neighbors
7.2 Optimal Margin Classifiers
7.3 Soft Margins
7.4 Nonlinearity and Kernel Functions
7.5 Least-Squares Formulation
7.6 Generalization Performance
7.7 Multiclass Problems
7.8 Loss Functions
7.9 Computational Complexity
8 Regression Analysis
8.1 Linear Least-Squares
8.2 Nonlinear Regression
8.3 Nonparametric Regression
8.4 Computational Complexity
9 Boosting
9.1 Weak Classifiers
9.2 AdaBoost
9.3 A Family of Convex Boosters
9.4 Nonconvex Loss Functions

Part III: Quantum Computing and Machine Learning

10 Clustering Structure and Quantum Computing
10.1 Quantum Random Access Memory
10.2 Calculating Dot Products
10.3 Quantum Principal Component Analysis
10.4 Towards Quantum Manifold Embedding
10.5 Quantum K-Means
10.6 Quantum K-Medians
10.7 Quantum Hierarchical Clustering
10.8 Computational Complexity
11 Quantum Pattern Recognition
11.1 Quantum Associative Memory
11.2 The Quantum Perceptron
11.3 Quantum Neural Networks
11.4 Physical Realizations
11.5 Computational Complexity
12 Quantum Classification
12.1 Nearest Neighbors
12.2 Support Vector Machines with Grover's Search
12.3 Support Vector Machines with Exponential Speedup
12.4 Computational Complexity
13 Quantum Process Tomography
13.1 Channel-State Duality
13.2 Quantum Process Tomography
13.3 Groups, Compact Lie Groups, and the Unitary Group
13.4 Representation Theory
13.5 Parallel Application and Storage of Unitary
13.6 Optimal State for Learning
13.7 Applying the Unitary
14 Boosting and Adiabatic Quantum Computing
14.1 Quantum Annealing
14.2 Quadratic Unconstrained Binary Optimization
14.3 Ising Model
14.4 QBoost
14.5 Nonconvexity
14.6 Sparsity and Generalization Performance
14.7 Mapping to Hardware
14.8 Computational Complexity


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