1 INTRODUCTION

2 QUANTUM-INSPIRED MACHINE LEARNING
2.1 "Quantum-Inspired"
2.2 "Machine Learning"
2.3 "Quantum-Inspired Machine Learning"

3 SELECTION CRITERIA

4 CURRENT QIML TECHNIQUES
4.1 Dequantized Algorithms
4.2 Tensor Networks
4.3 Quantum Variational Algorithm Simulation (QVAS)
4.4 Other QiML Methods

5 QIML IN PRACTICE
5.1 Image Modeling
5.2 Natural Language Processing
5.3 Medical
5.4 Finance
5.5 Physics
5.6 Chemistry
5.7 Cybersecurity
5.8 Other Tasks and Applications
5.9 Limiting Factors on the Use of QiML in Practice

6 PARALLELS BETWEEN QIML MODELS AND CONVENTIONAL CLASSICAL MODELS
6.1 Tensor Networks
6.2 Quantum Variational Algorithm Simulation
6.3 Dequantized Algorithms
6.4 Other QiML Methods
6.5 Levels of Quantum Mechanics Integration

7 AVAILABLE RESOURCES FOR QIML IMPLEMENTATION
7.1 Research Implementations
7.2 Toolboxes
7.3 Commercial Applications

8 OPEN ISSUES

9 CONCLUSION