Lilly Tech Systems
Learn Quantum Machine Learning
Explore the intersection of quantum computing and machine learning. Master quantum circuits, variational algorithms, Qiskit, PennyLane, and hybrid quantum-classical models — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Quantum ML? The promise of quantum advantage for machine learning tasks.
2. Quantum Computing Basics
Qubits, superposition, entanglement, and quantum gates explained for ML practitioners.
3. Quantum Circuits for ML
Variational quantum circuits, parameterized gates, and hybrid quantum-classical optimization.
4. Qiskit
Build quantum ML models with IBM's Qiskit framework and run them on real quantum hardware.
5. PennyLane
Differentiable quantum programming with PennyLane for quantum neural networks and QML.
6. Best Practices
Noise mitigation, circuit optimization, simulator vs hardware, and production QML workflows.
What You'll Learn
By the end of this course, you will be able to:
Quantum Foundations
Understand qubits, superposition, entanglement, and how quantum computing accelerates ML.
Build Quantum Circuits
Design variational quantum circuits and parameterized quantum models for classification and regression.
Use Real Frameworks
Implement QML algorithms in Qiskit and PennyLane, running on simulators and real quantum hardware.
Production QML
Apply noise mitigation, select the right quantum backends, and integrate quantum models into ML pipelines.