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Introduction

Quantum machine learning (QML) has emerged as a promising field at the intersection of quantum computing and machine learning. With the potential for exponential speedup in certain computational tasks, quantum algorithms have the ability to surpass classical methods in various applications, including classification. Quantum classification, a subset of QML, aims to leverage quantum algorithms and techniques to improve the accuracy and efficiency of classification tasks. This thesis explores the development and application of quantum machine learning techniques for quantum classification.

1.1 Introduction

1.2 Background of study

1.3 Problem Statement

1.4 Objective of study

1.5 Limitation of study

1.6 Scope of study

1.7 Significance of study

1.8 Structure of the Thesis

1.9 Definition of Terms

Chapter 2: Literature Review

2.1 Overview of Quantum Machine Learning

2.2 Quantum Classification Algorithms

2.3 Quantum Feature Mapping Techniques

2.4 Quantum Support Vector Machines

2.5 Quantum Neural Networks

2.6 Applications of Quantum Classification

2.7 Quantum Computing Platforms

2.8 Challenges in Quantum Machine Learning

2.9 Quantum Machine Learning in Industry

2.10 Future Directions in Quantum Classification

Chapter 3: Research Methodology

3.1 Research Design

3.2 Data Collection

3.3 Data Preprocessing

3.4 Feature Selection

3.5 Quantum Algorithm Selection

3.6 Model Training and Testing

3.7 Evaluation Metrics

3.8 Performance Analysis

Chapter 4: Discussion of Findings

4.1 Performance Comparison of Quantum vs Classical Classification

4.2 Impact of Quantum Feature Mapping Techniques

4.3 Scalability of Quantum Machine Learning Algorithms

4.4 Robustness of Quantum Classification Models

4.5 Interpretability of Quantum Machine Learning Models

Chapter 5: Conclusion and Summary

In conclusion, this thesis explores the exciting potential of quantum machine learning for quantum classification. By analyzing the current state of the field, conducting empirical studies, and discussing the findings, we aim to contribute to the advancement of quantum classification algorithms and applications. This thesis provides valuable insights for researchers and practitioners in the field of quantum computing and machine learning, paving the way for future innovations in quantum classification.

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