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PennyLane Codebook: Interactive Quantum ML Learning
Xanadu / PennyLane Team
2 courses · 6 tutorials
course
Xanadu / PennyLane Team
course
Xanadu / Community
Train a variational quantum classifier on the Iris dataset using PennyLane with the Amazon Braket backend, including local simulation, SV1 managed simulator, and Hybrid Jobs for persistent classical-quantum training loops.
Understand why barren plateaus occur in deep variational circuits, how to detect them in PennyLane, and practical strategies to avoid them including layerwise training and structured ansatze.
Run your first differentiable quantum circuit in PennyLane. Build a Bell state, compute gradients, and see why PennyLane is the go-to framework for quantum ML.
A conceptual and practical introduction to quantum machine learning: what QML is, data encoding strategies, parameterized quantum circuits, and a complete classification example.
Train variational quantum circuits directly on realistic noise models using PennyLane. Compare circuits trained with and without noise, insert depolarizing and amplitude damping channels, and apply noise injection techniques to improve real hardware performance.
Build a complete hybrid quantum-classical optimization pipeline: PennyLane QNode wrapped as a PyTorch layer, automatic differentiation through quantum circuits, Adam optimizer training on a binary classification task, and comparison to classical logistic regression.