This repository contains materials for a quantum clustering project that explores using quantum computing for data clustering tasks.
This project demonstrates how quantum computing techniques can be applied to clustering problems, comparing classical k-means with quantum-based implementations.
The main notebook quantum_clustering.ipynb contains the full implementation and explanations of:
- Classical k-means clustering and its limitations
- Quantum data encoding strategies
- Quantum k-means algorithm implementation
- Results analysis and visualization
The notebook also includes three interactive MCQ quizzes to test your understanding of:
- Classical k-means concepts
- Quantum encoding methods
- Quantum k-means algorithms
You can also open and run the notebook directly in Google Colab:
To access the MCQ quizzes, make sure to run both the first cell and the quiz cell
!git clone https://github.com/YoushaExT/quantum-clustering.git
%cd quantum-clustering
from helpers import create_kmeans_quiz, create_quantum_encoding_quiz, create_quantum_kmeans_quiz
The project requires Python with libraries including qiskit, matplotlib, numpy, and scikit-learn.