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Introduction

Quantum computing has emerged as a powerful tool in solving complex optimization problems in various fields, including finance. Financial portfolio optimization is a crucial task for investors to maximize returns while minimizing risks. Traditional optimization algorithms such as Markowitz mean-variance optimization have limitations in handling large datasets and non-convex optimization problems. Quantum-inspired algorithms, which are classical algorithms inspired by quantum computing principles, have shown promising results in solving optimization problems efficiently. This thesis explores the application of quantum-inspired algorithms for financial portfolio optimization.

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 Two: Literature Review
2.1 Introduction to Financial Portfolio Optimization
2.2 Traditional Optimization Algorithms in Finance
2.3 Quantum Computing Principles
2.4 Quantum-Inspired Algorithms
2.5 Applications of Quantum-Inspired Algorithms in Finance
2.6 Comparison between Quantum-Inspired Algorithms and Traditional Algorithms
2.7 Challenges and Limitations of Quantum-Inspired Algorithms
2.8 Current Research Trends in Quantum-Inspired Algorithms
2.9 Importance of Portfolio Optimization in Finance
2.10 Summary of Literature Review

Chapter Three: Research Methodology
3.1 Introduction to Research Methodology
3.2 Data Collection and Preprocessing
3.3 Selection of Quantum-Inspired Algorithms
3.4 Model Development and Implementation
3.5 Evaluation Metrics
3.6 Experimental Design
3.7 Data Analysis Techniques
3.8 Validation and Sensitivity Analysis

Chapter Four: Discussion of Findings
4.1 Overview of Experimental Results
4.2 Performance Comparison of Quantum-Inspired Algorithms
4.3 Impact of Quantum-Inspired Algorithms on Portfolio Optimization
4.4 Sensitivity Analysis Results
4.5 Interpretation of Results
4.6 Implications for Financial Industry
4.7 Recommendations for Future Research
4.8 Limitations of the Study

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contribution to the Field
5.3 Practical Implications
5.4 Limitations and Future Research Directions
5.5 Conclusion

Thesis Overview on Quantum-Inspired Algorithms for Financial Portfolio Optimization

The financial industry is constantly evolving, with investors seeking innovative ways to optimize their portfolios to achieve maximum returns with minimum risks. Traditional optimization algorithms have limitations in handling complex financial data and non-convex optimization problems. Quantum-inspired algorithms, which leverage principles from quantum computing, have shown potential in solving optimization problems efficiently. This thesis aims to explore the application of quantum-inspired algorithms for financial portfolio optimization.

The introduction will provide an overview of the research topic, including the background, problem statement, objectives, scope, and significance of the study. The literature review will delve into the current state of research on financial portfolio optimization, traditional optimization algorithms, quantum computing principles, and applications of quantum-inspired algorithms in finance. The research methodology chapter will outline the data collection process, selection of algorithms, model development, evaluation metrics, and data analysis techniques.

The discussion of findings chapter will present the experimental results, performance comparison of quantum-inspired algorithms, impact on portfolio optimization, sensitivity analysis, and implications for the financial industry. The conclusion and summary chapter will summarize the findings, discuss the contribution to the field, practical implications, limitations of the study, and recommendations for future research. This thesis aims to provide valuable insights into the potential of quantum-inspired algorithms for financial portfolio optimization and contribute to the growing body of research in this field.

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