Qiskit algorithm Notebook

This repository contains a collection of Jupyter notebooks that explore various quantum machine learning algorithms and their applications.

Contents:

1. QK-mean: An exploration into q-mean classification using the classic Iris dataset. Dive into the nuances of quantum clustering and how it can be applied to one of the most well-known datasets in machine learning.

2. QSVM-Genetics: This notebook showcases the power of Quantum Support Vector Machines (QSVM) in conjunction with genetic algorithms to generate optimal feature maps. Observe how combining quantum computing with evolutionary algorithms could help traditional machine learning techniques.

3. Satisfiability: This notebook use the Grover algorithm to solve the graph coloring problem. The problem is formulated as a SAT problem which can be used by qiskit to generate an oracle.

4. Sentiment Analysis: Explore the realm of text classification using QSVM coupled with a range of natural language processing (NLP) tools. This notebook sheds light on the potential of quantum computing in the domain of sentiment analysis that could lead to either more efficient or precise classification.

Getting Started:

To run these notebooks:

Ensure you have an appropriate quantum computing environment or library installed. Clone this repository. Navigate to the desired notebook and execute the cells.