From Classical Machine Learning → Quantum Computing → Quantum Machine Learning
An AI-powered, interactive learning platform designed to bridge the gap between Artificial Intelligence and Quantum Computing through visual learning, coding labs, simulations, and an intelligent AI tutor.
🌐 Live Demo: https://tanjinadnanabir.github.io/Quantum-Machine-Learning/
Video Demo: https://tinyurl.com/y72s62mn
Learning Quantum Machine Learning is often overwhelming because it requires knowledge from multiple disciplines.
Schrödinger's Cats provides a structured learning path that gradually takes learners from:
Artificial Intelligence
↓
Classical Machine Learning
↓
Quantum Computing
↓
Quantum Machine Learning
Everything is organized into one modern learning ecosystem.
The curriculum is divided into three progressive learning levels.
Build the mathematical and programming foundations required for QML.
- Linear Algebra & Vector Spaces
- Complex Numbers & Basic Calculus
- Python & NumPy Primer
Learn the fundamentals of AI, Machine Learning, and Quantum Computing.
Topics include:
- Introduction to Artificial Intelligence
- Why Machine Learning?
- Classical Machine Learning
- Why Quantum Computing?
- Quantum Computing Foundations
- Beginner Coding Project
Bridge classical AI with Quantum Computing.
Topics include:
- Introduction to Quantum Machine Learning
- Why Quantum Machine Learning?
- Quantum Data Encoding
- Variational Quantum Circuits
- Intermediate Project
Apply QML to real-world research and innovation.
Topics include:
- Real-World QML Applications
- Advanced Quantum Machine Learning
- Research & Capstone Project
- Final Capstone
The platform includes 15+ interactive features, including:
- 🧠 AI-powered Quantum Tutor
- ⚛️ Quantum Circuit Simulator
- 📊 Interactive Visualizations
- 💻 Hands-on Coding Labs
- 📝 Adaptive Quizzes
- 📈 Learning Analytics Dashboard
- ☁️ Real Quantum Hardware Access
- 🎮 QML Playground
- 📚 Research Hub
- ⚡ Quantum Simulations
- 👨🏫 Educator Toolkit
- ♿ Accessibility Features
- 📖 Interactive Tutorials
- 📂 Datasets & Benchmarks
- 🏆 Challenge Projects
The learning platform also provides:
- Interactive tutorials
- Jupyter notebooks
- Coding exercises
- Challenge projects
- Research datasets
- Benchmark collections
- Learning resources
| Category | Technologies |
|---|---|
| Programming | Python |
| AI/ML | NumPy, Scikit-Learn |
| Quantum | PennyLane, Qiskit |
| Visualization | Matplotlib, Plotly |
| Frontend | HTML, CSS, JavaScript |
| Learning | Interactive Labs & Simulations |
After completing the curriculum, learners will be able to:
- Understand Quantum Computing fundamentals
- Build classical Machine Learning models
- Design Quantum Circuits
- Implement Quantum Machine Learning algorithms
- Train Variational Quantum Circuits
- Work with PennyLane and Qiskit
- Apply QML to real-world research problems
Visit the platform:
Contributions are always welcome!
You can help by:
- Adding tutorials
- Fixing bugs
- Improving documentation
- Creating notebooks
- Adding visualisations
- Implementing research papers
- Building new QML projects
If you'd like to contribute, please open an Issue first before submitting a Pull Request.
Licensed under the Apache 2.0 License.
@misc{schrodingerscats2026,
author = {Tanjin Adnan Abir},
title = {Schrödinger's Cats: The Open Learning Platform for Quantum Machine Learning},
year = {2026},
howpublished = {\url{https://tanjinadnanabir.github.io/Quantum-Machine-Learning/}}
}