Quantum Computing Tutorials

Welcome to the Quantum Computing Tutorials repository! This project aims to provide comprehensive educational materials, examples, and tools for learning and developing quantum algorithms using Qiskit.

Getting Started

To get started with quantum computing and this repository, follow the steps below:

Prerequisites

Ensure you have Python installed on your system. You can download Python from the official Python website.

Installation

1. Install Qiskit:

   Qiskit is the main quantum computing framework used in this repository. Install it using pip:

   pip install qiskit

2. Clone the Repository:

   Clone this repository to your local machine:

   git clone https://github.com/dkrizhanovskyi/quantum-computing-tutorials.git

3. Navigate to the Tutorials Directory:

   Navigate to the tutorials directory to start exploring the Jupyter notebooks:

   cd quantum-computing-tutorials/tutorials

Running the Tutorials

To run the Jupyter notebooks, you will need Jupyter installed. If you don't have it, you can install it using pip:

pip install jupyter

Then, start the Jupyter notebook server:

jupyter notebook

Open your browser and navigate to the tutorials directory to start exploring the notebooks.

Contents

This repository is organized into the following directories:

• tutorials/: Jupyter notebooks with step-by-step tutorials for learning quantum computing concepts and algorithms.
• examples/: Example implementations of well-known quantum algorithms. These examples provide practical insights and code to help you understand how to implement quantum algorithms using Qiskit.
• tools/: Utility scripts for common tasks in quantum computing, such as error analysis, visualization, and other helpful functions.

Examples

Here are some of the quantum algorithms and concepts covered in the examples:

• Grover's Algorithm: Demonstrates how to use Grover's search algorithm for searching an unsorted database.
• Quantum Fourier Transform: Shows the implementation of the Quantum Fourier Transform (QFT).
• Quantum Machine Learning: Implements a basic quantum machine learning algorithm using Qiskit.
• Shor's Algorithm: Uses Shor's algorithm to factorize integers.
• Quantum Error Correction: Explores the concept of quantum error correction and implements the three-qubit bit-flip code.

Contributing

We welcome contributions from the community! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Submit a pull request.

Please see CONTRIBUTING.md for more details on how to get involved.

License

This project is licensed under the MIT License. See the LICENSE file for more details.