The Quantum Portfolio Optimization Project explores the application of quantum computing techniques to solve complex portfolio optimization problems. The project leverages quantum algorithms to improve the efficiency and accuracy of portfolio selection by formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) and utilizing Quantum Approximate Optimization Algorithm (QAOA) as the solving mechanism.

• Introduction
• Project Objectives
• Usage
• Installation
• Contributors
• License

Quantum computing offers a novel approach to solving optimization problems that are challenging for classical computers. This project aims to harness the power of quantum computing to enhance portfolio optimization strategies in the financial domain.

• Formulate the portfolio optimization problem as a QUBO.
• Implement the Quantum Approximate Optimization Algorithm (QAOA) to solve the QUBO problem.
• Develop tools and libraries to facilitate quantum portfolio optimization.
• Compare quantum-based results with classical optimization methods for portfolio selection.
• Explore the scalability and performance of quantum portfolio optimization algorithms.

1. Clone the repository: git clone https://github.com/yourusername/quantum-portfolio-optimization.git
2. Navigate to the project directory: cd quantum-portfolio-optimization
3. Install required dependencies: pip install -r requirements.txt
4. Run the quantum portfolio optimization script: python quantum_portfolio_optimization.py

This project requires Python 3.x and the following packages:

• Qiskit (Quantum Computing Framework)
• NumPy (Numerical Computing Library)
• Matplotlib (Plotting Library)

To install the necessary packages, run:

pip install -r requirements.txt

This project is licensed under the MIT License.

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