QAOA for Tail-Assignment Problem

This repository contains the coursework and simulation code for the project: Applying QAOA to the Tail-Assignment Problem, completed as part of the Quantum Machine Learning (IT-401) course under the guidance of Prof. Manjunath Joshi.

Group Members

• Kavy Sanghani (202301044)
• Khush Patel (202301142)
• Rishank Dudhat (202301068)

Overview

The tail-assignment problem is inherently combinatorial and computationally expensive for classical methods at scale. Quantum algorithms like QAOA offer a promising approach by leveraging quantum superposition and optimization techniques to explore large solution spaces more efficiently.

In this project, we:

• Formulate the problem in a quantum-compatible framework.
• Construct the corresponding cost Hamiltonian for the Exact Cover formulation.
• Analyze scalability and practical challenges.

Repository Contents

• Tail_Assignment_Problem_code.ipynb: The primary Jupyter Notebook containing the implementation of the Quantum Approximate Optimization Algorithm (QAOA) applied to the Tail-Assignment Problem.
• QML_QAOA_TAP.pdf: The research paper/report corresponding to the study and simulation.

Usage

To run the notebook locally, ensure you have Jupyter and the necessary quantum computing libraries installed.

# Clone the repository
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

# Run the notebook
jupyter notebook Tail_Assignment_Problem_code.ipynb

References

• Applying QAOA to the Tail-Assignment Problem