Welcome to Quantum Computing Coding, a complete repository for students, developers, and researchers who want to learn and implement quantum algorithms from scratch using Python-based frameworks Qiskit.
This repository provides:
- Quantum algorithm implementations
- Jupyter notebooks for interactive practice
- Detailed theory notes
- Hands-on coding exercises
- Clear folder structure for easy learning and collaboration
This repository is designed to help you:
- Learn the fundamentals of quantum computing
- Practice coding quantum algorithms
- Understand how quantum circuits work
- Run simulations and real quantum hardware
- Prepare for exams, interviews, and research projects
- Collaborate with the open-source quantum computing community
The repository includes the following algorithms:
- Deutsch–Jozsa Algorithm
- Grover’s Search Algorithm
- Variational Quantum Eigensolver (VQE)
- Quantum Approximate Optimization Algorithm (QAOA)
- Quantum Phase Estimation (QPE)
Each algorithm includes:
- Python implementation
- Notebook tutorial
- Theory notes
- Step-by-step explanations
Install the quantum frameworks of your choice,in this we work with:
Qiskit
pip install qiskit
Open Jupyter Notebook: jupyter notebook notebooks/grover.ipynb
All quantum concepts And algorithm theory notes related with qiskit implemtation of each code are included in: 'dayNo_Topic/dayNo_notes.md' like this format file according to topic, inside each topic folder. It includes:
- Algorithm theory and purpose
- Prerequisites
- Circuit design and explanation
- Mathematical intuition
By working with this repository, you will learn:
- Quantum circuit design and implementation
- Superposition, entanglement, and measurement
- Building and running quantum algorithms
- Hybrid quantum-classical methods (VQE/QAOA)
- Quantum simulation and real backend execution
- Python coding skills for quantum computing
- Provide a hands-on, beginner-to-advanced quantum computing learning resource
- Help students and professionals practice and implement algorithms
- Create an open-source, collaborative environment for quantum learning
- Encourage contributions and knowledge sharing in the quantum community
Contributions are highly welcome! You can contribute by:
- Adding new quantum algorithms or examples
- Improving code structure or style
- Writing Jupyter notebooks for tutorials
- Fixing bugs or typos
- Improving documentation
- Adding diagrams or visualizations
Please see CONTRIBUTING.md for detailed guidelines.
#Support & Help If you need help:
- GitHub Issues → Report bugs or ask questions
- GitHub Discussions → Ask conceptual or practical questions
- Contact Maintainer → via GitHub profile
See SUPPORT.md for complete instructions.
If this repository helps you:
- Give it a Star ⭐ on GitHub
- Share it with friends, classmates, or communities
- Collaborate and submit pull requests
- Your support helps grow the repository and the quantum learning community!
This project is released under the MIT License, allowing free use, modification, and distribution with proper attribution.
Name: Shreya Sunil Palase
GitHub: @shreyapalase
You are welcome to reach out for:
- Questions about this repository
- Collaboration opportunities
- Suggestions for new content
- Feedback and contributions
Your interest, contributions, and support help make quantum learning accessible to everyone. Together, we can learn, code, and explore the world of quantum computing—one qubit at a time!
Thank you for visiting Quantum Computing Coding!