I. INTRODUCTION  
A. Scope and Contributions  
B. Outline  

II. QUANTUM COMPUTING PRELIMINARIES  
A. Understanding the Qubit  
B. Superposition  
C. Quantum Gates  
E. Quantum Correlations  
F. Quantum Noise  
G. Quantum Stack  

III. BENEFITS OF QUANTUM COMPUTING  
A. Quantum Dimensionality Reduction  
B. Quantum Supremacy for Solving Complex Problems  
C. Quantum Enhancement to Classical Problems  
D. Preserving Maximum Information  
E. Large-Scale Infrastructures and Design Automation  

IV. QUANTUM MACHINE LEARNING ALGORITHMS  
A. Categorization of QML Approaches  
B. Parameterized Quantum Circuits  
C. Quantum Optimization Problems  
D. Quantum Neural Networks  
E. Quantum Kernels  
F. Quantum Computing for Bayesian Machine Learning  
G. Limitations of Current QML Approaches  

V. DATASETS  
A. Quantum Data  
B. Motivation for QML Datasets  
C. Data Pre-Processing  
D. Data Encoding  
E. Quantum Datasets Collection  

VI. TOOLS AND TECHNOLOGIES  
B. Quantum Hardware  
C. Quantum Circuit Mapping  
D. Quantum Simulators and Primitives  
E. Supporting Infrastructure Tools and Hardware  

VII. APPLICATIONS  
A. Molecular Simulation: Drug Design  
B. Quantum Sensing  
C. Quantum Cryptography  
D. Financial Modeling  
E. Logistics Optimization  
F. Environmental, Social, and Governance (ESG)  

VIII. CONCLUSION AND ROAD AHEAD