Introduction to Quantum Machine Learning for TSP using QAOA

This repository contains a Jupyter Notebook titled qml_tsp_intro.ipynb, which provides an introduction to solving the Traveling Salesperson Problem (TSP) using Quantum Machine Learning (QML) and the Quantum Approximate Optimization Algorithm (QAOA).

Overview

The Traveling Salesperson Problem (TSP) is a combinatorial optimization problem where the goal is to find the shortest route visiting all given cities exactly once and returning to the starting point. This notebook leverages QAOA, a hybrid quantum-classical algorithm, to solve the TSP using quantum computing frameworks.

Features

• Introduction to Quantum Machine Learning: Demonstrates foundational QML concepts applied to optimization problems.
• TSP Formulation: Maps the TSP into a quantum optimization problem solvable using QAOA.
• Hybrid Quantum-Classical Approach: Combines quantum algorithms with classical optimization techniques.
• QAOA Implementation: Detailed setup and execution of the Quantum Approximate Optimization Algorithm for TSP.
• Visualization: Graphical representations of the TSP solution and QAOA optimization progress.

Requirements

To run the notebook, you will need:

• Python 3.8 or higher
• Jupyter Notebook or JupyterLab
• Required Python libraries:
  • numpy
  • matplotlib
  • qiskit or pennylane (for QAOA implementation)
  • tensorflow or other ML libraries if applicable

You can install the dependencies using:

pip install numpy matplotlib qiskit pennylane tensorflow