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Introduction

Quantum computing is a revolutionary field that has the potential to significantly impact various industries, including finance. Portfolio optimization is a critical aspect of financial decision-making, aiming to maximize returns while minimizing risk. Traditional optimization techniques often face challenges in dealing with the complexities of financial markets. Quantum-inspired algorithms offer a promising solution by harnessing the principles of quantum mechanics to solve optimization problems efficiently.

Chapter 1: Introduction

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 Introduction to portfolio optimization
2.2 Traditional optimization techniques in finance
2.3 Introduction to quantum computing
2.4 Quantum-inspired algorithms in finance
2.5 Applications of quantum-inspired algorithms in portfolio optimization
2.6 Comparison between quantum-inspired and classical optimization methods
2.7 Challenges and limitations of quantum-inspired algorithms
2.8 Current research trends in quantum-inspired portfolio optimization
2.9 Case studies of quantum-inspired algorithms in finance
2.10 Future prospects of quantum-inspired algorithms in portfolio optimization

Chapter 3: Research Methodology

3.1 Research design
3.2 Data collection methods
3.3 Sampling techniques
3.4 Model development
3.5 Implementation of quantum-inspired algorithms
3.6 Performance evaluation metrics
3.7 Sensitivity analysis
3.8 Validation of results

Chapter 4: Discussion of Findings

4.1 Analysis of empirical results
4.2 Comparison with traditional optimization methods
4.3 Interpretation of findings
4.4 Implications for financial decision-making
4.5 Practical applications of quantum-inspired algorithms
4.6 Recommendations for future research
4.7 Limitations of the study
4.8 Potential areas for improvement

Chapter 5: Conclusion and Summary

In conclusion, this thesis explores the application of quantum-inspired algorithms for portfolio optimization in finance. The study highlights the potential of quantum computing to revolutionize the field of financial decision-making by offering efficient solutions to complex optimization problems. By analyzing the current literature, developing a research methodology, and discussing the findings, this thesis aims to contribute to the growing body of knowledge on quantum-inspired algorithms in finance.

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