The most comprehensive, beginner-friendly quantum computing course with 45 examples covering everything from "what is a qubit?" to industry applications in drug discovery and financial optimization.
Never studied quantum mechanics? No problem! This course is designed for software developers, students, and professionals who want to understand quantum computing without needing a PhD in physics.
- 🎓 Zero Prerequisites: Assumes no quantum mechanics or advanced math background
- 🛠️ Hands-On Learning: Learn by running real quantum programs, not just reading theory
- 📈 Gentle Learning Curve: Carefully designed progression from basic concepts to advanced applications
- 🐛 Beginner-Focused: Includes debugging guides, common mistakes, and troubleshooting
- 📊 Rich Visualizations: Beautiful plots, Bloch spheres, and circuit diagrams make concepts clear
- ⚡ Real-World Ready: Industry applications across chemistry, finance, cryptography, and AI
Unlike other courses that oversell quantum computing, we give you an honest assessment of:
- What quantum computers can and cannot do today
- Realistic timeline for practical applications (hint: we're still early!)
- Current hardware limitations and why they matter
- Why learning quantum computing now still makes sense for your career
👉 Read the Complete Beginner's Guide - Your roadmap to quantum computing mastery
Essential First Steps:
- Hardware Reality Check: Run
python examples/module1_fundamentals/08_hardware_reality_check.py - Your First Qubit: Run
python examples/module1_fundamentals/01_classical_vs_quantum_bits.py - Quantum "Magic": Run
python examples/module1_fundamentals/07_no_cloning_theorem.py
- Python 3.11 or higher (3.12+ recommended for best performance)
- Basic programming knowledge (if/else, loops, functions)
- Curiosity about the future of computing!
- New: Docker (optional) for containerized environments with GPU support
You do NOT need:
- ❌ PhD in quantum physics
- ❌ Advanced linear algebra
- ❌ Expensive quantum computer
# Clone the repository
git clone https://github.com/AIComputing101/quantum-computing-101.git
cd quantum-computing-101
# Build and run CPU container
cd docker
./build.sh cpu
./run.sh -v cpu -e module1_fundamentals/01_classical_vs_quantum_bits.py
# Or start interactive session
./run.sh -v cpu -i# Clone the repository
git clone https://github.com/AIComputing101/quantum-computing-101.git
cd quantum-computing-101
# Install core dependencies (recommended for beginners)
pip install -r examples/requirements-core.txt
# Test your setup
python examples/module1_fundamentals/01_classical_vs_quantum_bits.py
# Verify all examples work (optional)
python verify_examples.py --quick# NVIDIA GPU acceleration (5-8x speedup with CUDA 12.6)
cd docker
./build.sh gpu-nvidia
./run.sh -v gpu-nvidia -e module6_machine_learning/01_quantum_neural_network.py
# AMD ROCm GPU acceleration (ROCm 6.x with MI300 series support)
./build.sh gpu-amd
./run.sh -v gpu-amd -e module6_machine_learning/01_quantum_neural_network.pyPerfect for complete beginners - now with enhanced explanations and reality checks:
| Module | Topic | Examples | Key New Features |
|---|---|---|---|
| Module 1 | Quantum Fundamentals | 8 ⭐ | NEW: No-Cloning, Hardware Reality, Enhanced explanations |
| Module 2 | Mathematical Foundations | 5 | Enhanced intuitive explanations |
| Module 3 | Quantum Programming | 6 ⭐ | NEW: Complete Debugging Guide for beginners |
🌟 New Beginner-Essential Examples:
07_no_cloning_theorem.py- Why quantum is fundamentally different08_hardware_reality_check.py- What QC can/can't do today06_quantum_debugging_guide.py- Essential troubleshooting for beginners
Build algorithmic expertise:
| Module | Topic | Examples | Lines of Code |
|---|---|---|---|
| Module 4 | Quantum Algorithms | 5 | 1,843 |
| Module 5 | Error Correction | 5 | 2,111 |
| Module 6 | Quantum Machine Learning | 5 | 3,157 |
Real-world applications and quantum cryptography:
| Module | Topic | Examples | Key New Features |
|---|---|---|---|
| Module 7 | Hardware & Cloud | 5 | Enhanced hardware compatibility fixes |
| Module 8 | Industry Applications | 6 ⭐ | NEW: BB84 Quantum Cryptography |
🔐 New Real-World Example:
06_quantum_cryptography_bb84.py- Secure quantum key distribution protocol
# Local installation
python examples/module8_applications/01_quantum_chemistry_drug_discovery.py
# Docker
./docker/run.sh -v cpu -e module8_applications/01_quantum_chemistry_drug_discovery.pySimulate molecular systems for drug discovery using VQE (Variational Quantum Eigensolver).
# Local installation
python examples/module8_applications/02_financial_portfolio_optimization.py
# Docker with NVIDIA GPU acceleration
./docker/run.sh -v gpu-nvidia -e module8_applications/02_financial_portfolio_optimization.pyOptimize investment portfolios using QAOA (Quantum Approximate Optimization Algorithm).
# Local installation
python examples/module8_applications/04_cryptography_cybersecurity.py
# Docker
./docker/run.sh -v cpu -e module8_applications/04_cryptography_cybersecurity.pyImplement quantum key distribution protocols (BB84, E91) and post-quantum cryptography.
# Local installation
python examples/module4_algorithms/02_grovers_search_algorithm.py
# Docker
./docker/run.sh -v cpu -e module4_algorithms/02_grovers_search_algorithm.pyExperience quadratic speedup in unstructured search problems.
- 🎯 Advanced GPU Support: NVIDIA CUDA 12.6 + AMD ROCm 6.x
- ⚡ Zero Setup: No Python installation required
- 🚀 GPU Acceleration: 5-8x speedup for large simulations
- 🔄 Reproducible: Identical environment across all machines
- ☁️ Cloud Ready: Easy deployment to AWS/GCP/Azure
- 📊 Three Variants: CPU (1.2GB), NVIDIA GPU (3.5GB), AMD ROCm (3.2GB)
- 🏗️ Latest Hardware: Supports NVIDIA H100/A100 and AMD MI300A/MI300X GPUs
- Interactive Bloch sphere representations
- Circuit diagrams with detailed annotations
- Measurement probability histograms
- Algorithm performance comparisons
- Quantum state evolution animations
- Comprehensive CLI interfaces with argparse
- Robust error handling and informative messages
- Extensive docstrings and inline comments
- Object-oriented design with reusable components
- Unit tests and validation checks
- IBM Quantum cloud platform examples
- AWS Braket integration tutorials
- Real quantum device noise analysis
- Hardware-optimized circuit compilation
quantum-computing-101/
├── README.md # This file
├── LICENSE # Apache 2.0 License
├── CHANGELOG.md # Version history and updates
├── modules/ # Theoretical curriculum
│ ├── Module1_Quantum_Fundamentals.md
│ ├── Module2_Mathematical_Foundations.md
│ ├── ...
│ └── REFERENCE.md # Comprehensive reference guide
├── examples/ # Hands-on implementations (45 examples)
│ ├── README.md # Examples overview
│ ├── requirements-core.txt # Core dependencies for beginners (Updated v2.0)
│ ├── requirements.txt # All dependencies (Updated v2.0)
│ ├── requirements-dev.txt # Development tools
│ ├── module1_fundamentals/ # 8 beginner examples
│ ├── module2_mathematics/ # 5 math examples
│ ├── module3_programming/ # 6 programming examples
│ ├── module4_algorithms/ # 5 algorithm examples
│ ├── module5_error_correction/# 5 error correction examples
│ ├── module6_machine_learning/# 5 ML examples
│ ├── module7_hardware/ # 5 hardware examples
│ ├── module8_applications/ # 6 industry examples
│ └── utils/ # Shared utilities
├── docker/ # **NEW v2.0** - Complete containerization
│ ├── README.md # Docker setup guide
│ ├── requirements/ # Modular requirements for Docker
│ │ ├── base.txt # Core frameworks for all variants
│ │ ├── cpu.txt # CPU optimizations
│ │ ├── gpu-nvidia.txt # NVIDIA CUDA 12.6 packages
│ │ └── gpu-amd.txt # AMD ROCm 6.x packages with MI300 support
│ ├── Dockerfile.cpu # Lightweight CPU container (1.2GB)
│ ├── Dockerfile.gpu-nvidia # NVIDIA CUDA 12.6 container (3.5GB)
│ ├── Dockerfile.gpu-amd # AMD ROCm 6.x container with MI300 support (3.2GB)
│ ├── Dockerfile.base # Multi-stage base image
│ ├── docker-compose.yml # Complete orchestration
│ ├── build.sh # Smart build script with GPU detection
│ └── run.sh # Comprehensive container runner
├── verify_examples.py # Quality assurance tool
├── BEGINNERS_GUIDE.md # Complete learning pathway (Updated v2.0)
└── docs/ # Additional documentation
├── CONTRIBUTING.md # Contribution guidelines
├── CODE_OF_CONDUCT.md # Community standards
└── SECURITY.md # Security policy
We welcome contributions! Please see our Contributing Guidelines for details.
- 🐛 Bug Reports: Found an issue? Let us know!
- ✨ Feature Requests: Ideas for new examples or improvements
- 📚 Documentation: Help improve explanations and tutorials
- 🧪 Testing: Test examples on different platforms
- 🎨 Visualizations: Create new ways to visualize quantum concepts
- 🔧 Performance: Optimize simulation speed and memory usage
# Clone and install development dependencies
git clone https://github.com/AIComputing101/quantum-computing-101.git
cd quantum-computing-101
pip install -r examples/requirements-dev.txt
# Verify all examples work
python verify_examples.py
# Run specific module tests
python verify_examples.py --module module1_fundamentals- Follow the progressive module structure
- Run examples to reinforce theoretical concepts
- Experiment with parameters to deepen understanding
- Complete exercises at the end of each module
- Comprehensive curriculum ready for classroom use
- Detailed instructor notes in each module
- Exercises and assessment materials
- Flexible module structure for different course lengths
- Production-ready implementations of quantum algorithms
- Extensible framework for algorithm development
- Benchmarking tools for performance analysis
- Integration with popular quantum computing frameworks
- 📚 8 Complete Modules: Comprehensive learning progression from basics to advanced applications
- 💻 45 Production Examples: All examples fully implemented and tested
- 🎯 100% Compatibility: All examples verified with current Qiskit versions
- 🌍 Multi-Platform: Linux, macOS, Windows support
- 🔧 Quality Verified: Automated verification tool ensures all examples work
- 📈 Educational Impact: Designed for students, professionals, and complete beginners
- Qiskit: IBM's quantum computing framework
- Cirq: Google's quantum computing framework
- PennyLane: Quantum machine learning framework
- Quantum Open Source Foundation: Community-driven quantum software
- 🐛 Technical Issues: Run
python verify_examples.pyto diagnose problems - 📚 Learning Questions: Check the Complete Beginner's Guide
- 💬 Community Support: Join quantum computing forums and communities
- 🔧 Installation Problems: Follow the setup instructions above
- Qiskit Textbook - Comprehensive quantum computing resource
- IBM Quantum Experience - Run on real quantum computers
- Quantum Computing Stack Exchange - Q&A community
If you use this project in your research, education, or publications, please cite it as:
@misc{quantum-computing-101,
title={Quantum Computing 101: A Comprehensive Beginner-Friendly Course},
author={{Stephen Shao}},
year={2025},
howpublished={\url{https://github.com/AIComputing101/quantum-computing-101}},
note={A complete quantum computing educational resource with production-ready examples covering fundamentals to advanced applications}
}This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
- IBM Qiskit Team: For the excellent quantum computing framework
- Quantum Computing Community: For inspiration and feedback
- Open Source Contributors: For making this project better
If you find Quantum Computing 101 helpful, please give it a star ⭐ to help others discover it!
Ready to start your quantum journey? Jump to Quick Start or explore the examples directory!
Quantum Computing 101 - Making quantum computing accessible to everyone 🚀⚛️🌍