Quantum Portfolio Optimization Research

This repository contains research on portfolio optimization using quantum computing approaches, specifically implementing and comparing Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) methods.

Overview

The project implements quantum approaches to portfolio optimization using Google's Cirq framework. It compares different methods:

• Classical portfolio optimization
• VQE-based optimization
• QAOA-based optimization

Features

• Implementation of VQE and QAOA circuits for portfolio optimization
• Comparison of classical vs quantum approaches
• Performance metrics including returns, risk, and Sharpe ratios
• Visualization of portfolio weights and performance metrics
• Extensible framework for testing different quantum optimization strategies

Requirements

The project requires the following Python packages:

• cirq
• sympy
• numpy
• matplotlib
• plotly
• typing

Installation

1. Clone this repository:

git clone https://github.com/yourusername/quantum-portfolio-optimization.git
cd quantum-portfolio-optimization

2. Install required packages:

pip install -r requirements.txt

Usage

The main research implementation is in the Jupyter notebook QCFinanceResearch.ipynb. To run it:

1. Start Jupyter Lab or Notebook:

jupyter lab

2. Open QCFinanceResearch.ipynb
3. Run all cells to see the comparison of different optimization approaches

Results

The implementation compares three approaches:

1. Classical optimization (equal weights)
2. VQE optimization
3. QAOA optimization

Key findings show that:

• VQE tends to find more concentrated portfolios
• QAOA produces intermediate concentration levels
• Each method has different trade-offs in terms of returns, risk, and computation time

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this code in your research, please cite:

@software{quantum_portfolio_optimization,
  author = {Dhairya Patel},
  title = {Quantum Portfolio Optimization Research},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/yourusername/quantum-portfolio-optimization}
}