1 Introduction

2 Application Domains for Quantum Anomaly Detection

3 Quantum Algorithms and Quantum Advantage

4 Encoding Data with Quantum Systems  
4.1 From Bits to Qubits  
4.2 Qudits  
4.3 Qumodes (Continuous Variables)  
4.4 Embedding into QUBO Problems  

5 Processing Quantum Information with Quantum Computers  
5.1 Circuital Model  
5.2 Adiabatic Quantum Model  

6 Emulation of Quantum Computing Resources by High-Performance Computing

7 Generalization of Neural Networks in Quantum Circuits  
7.1 Variational Approach  
7.2 Neuron into Qubits  

8 Quantum Supervised Learning  
8.1 Quantum Feed-forward Neural Network for Binary Decisions  
8.2 Quantum Restricted Boltzmann Machine  
8.3 Neural Networks in Continuous Variables  
8.4 Classification with Qudits  
8.5 Classical and Quantum Support Vector Machines  
8.6 Natively Quantum Kernel Methods  

9 Quantum Unsupervised Learning  
9.1 QGAN for Anomaly Detection  
9.2 Other Approaches to Quantum Anomaly Detection by Unsupervised Learning Methods  

10 Quantum Approximate Optimization Algorithm  
10.1 The MaxCut Problem  
10.2 QAOA Formulation  

11 Quantum Reinforcement Learning  
11.1 Quantum Adaptation Algorithm  

12 Concluding Remarks