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PennyLane Quantum Machine Learning Demos
Xanadu / Community
3 courses · 9 tutorials
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Xanadu / Community
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Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
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Hasso Plattner Institute / IBM Quantum
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.
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.
Implement the Quantum Approximate Optimisation Algorithm to solve the MaxCut graph problem using PennyLane, and understand how QAOA bridges quantum computing and combinatorial optimisation.
Formulate mean-variance portfolio optimization as a QUBO, solve it with QAOA using Qiskit Optimization, and compare results against the classical Markowitz efficient frontier.
Run variational quantum algorithms using Amazon Braket Hybrid Jobs: managed classical compute + quantum access in one job, with checkpointing and cost tracking.
Implement the Quantum Approximate Optimization Algorithm for the Max-Cut problem in PennyLane: graph encoding, cost Hamiltonian, circuit construction, and parameter optimization.
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.
Use Qiskit's optimization module to formulate and solve combinatorial problems: Max-Cut, knapsack, and TSP using QAOA and the MinimumEigenOptimizer.
Implement the Quantum Approximate Optimization Algorithm to solve a small Max-Cut problem using Cirq, with parameterized circuits and classical optimization.