PennyLane Quantum Machine Learning Demos
Xanadu / Community
3 courses · 9 tutorials
Xanadu / Community
Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
Hasso Plattner Institute / IBM Quantum
Implement the Quantum Approximate Optimisation Algorithm to solve the MaxCut graph problem using PennyLane, and understand how QAOA bridges quantum computing and combinatorial optimisation.
Build the Quantum Approximate Optimization Algorithm from scratch in Qiskit to solve MaxCut on small graphs. Understand the circuit structure, cost function, and how to tune the depth parameter p.
Run variational quantum algorithms using Amazon Braket Hybrid Jobs: managed classical compute + quantum access in one job, with checkpointing and cost tracking.
Build a complete Max-Cut solver using QAOA in Qiskit Optimization. Covers graph construction with NetworkX, QuadraticProgram formulation, QAOA via MinimumEigenOptimizer, and how solution quality changes with circuit depth p.
Learn how to build quantum kernel functions with PennyLane, use them with scikit-learn's SVM, and understand when quantum kernels might offer an advantage over classical kernels, with a full working classification example.
Formulate mean-variance portfolio optimization as a QUBO, solve it with QAOA using Qiskit Optimization, and compare results against the classical Markowitz efficient frontier.
Use Qiskit's optimization module to formulate and solve combinatorial problems: Max-Cut, knapsack, and TSP using QAOA and the MinimumEigenOptimizer.
Run QAOA on Max-Cut in PennyLane end to end: encode the graph as a cost Hamiltonian, parameterize the variational circuit, and optimize toward the maximum partition.
Implement the Quantum Approximate Optimization Algorithm to solve a small Max-Cut problem using Cirq, with parameterized circuits and classical optimization.