3 courses · 6 tutorials
PennyLane
Xanadu / PennyLane Team
Interactive browser-based coding exercises teaching quantum computing with PennyLane from scratch, spanning 15 modules from qubits and gates through Grover's and Shor's algorithms, error correction, and variational quantum algorithms.
PennyLane
Xanadu / Community
Xanadu's collection of hundreds of interactive PennyLane tutorials, including 50+ quantum machine learning demos covering VQE, QAOA, quantum kernels, quantum neural networks, and more.
Udemy
Hoang Quy La
A hands-on Udemy course by Hoang Quy La covering qubits, quantum gates, and quantum circuits in Python with Cirq and Qiskit, plus the Deutsch-Jozsa algorithm, Grover's algorithm, the quantum Fourier transform, quantum phase estimation, variational quantum circuits, and an introduction to quantum error correction.
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.
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.
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.
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.