Quantum Advantage in Financial Modeling

This project demonstrates how Quantum Algorithms can offer computational advantages over Classical Methods in solving finance-related problems such as portfolio optimization, risk analysis, and option pricing.

Project Overview

• Implemented a Quantum Algorithm (e.g., QAOA / VQE) for portfolio optimization.

• Compared the performance of Quantum vs Classical approaches.

• Visualized results using Composer Diagrams and comparative plots.

Key Features

• Quantum Speedup: Showcases how quantum computing can handle high-dimensional optimization problems faster.

• Financial Use Case: Applied to portfolio optimization and Monte Carlo simulations in finance.

• Comparison: Benchmarked runtime and efficiency of quantum algorithms against classical solvers.

Tech Stack

• Qiskit (latest version, 2.x)

• Python (NumPy, Matplotlib, Pandas)

• Jupyter Notebook

• GitHub for version control

Results

Quantum algorithm showed potential advantage in scaling for large problem sizes.

Classical algorithms become inefficient as the number of assets grows.

How to Run

Clone this repository:

git clone https://github.com/your-username/quantum-finance.git
cd quantum-finance

Install dependencies:

pip install -r requirements.txt

Run the Jupyter Notebook:

jupyter notebook

Visual Representation

Composer diagram comparing Quantum vs Classical approaches in finance:

Future Work

• Extend to real-world stock market datasets.

• Explore hybrid quantum-classical algorithms for better scalability.

• Apply to risk minimization and option pricing.

Contribution

Contributions are welcome! Feel free to fork this repo, open issues, and submit PRs.

References

• IBM Qiskit Documentation

• Research papers on Quantum Finance

• Tutorials on QAOA & VQE