Introduction to QAOA with PennyLane

Introduction to QAOA with PennyLane

This PennyLane tutorial is a gentle, thorough introduction to the Quantum Approximate Optimization Algorithm (QAOA), the leading near-term quantum heuristic for combinatorial optimization, using PennyLane's dedicated QAOA module. QAOA encodes an optimization problem's objective into a cost Hamiltonian whose ground state is the optimal solution, and prepares a parameterized trial state by alternating, for a chosen number of layers, an evolution under the cost Hamiltonian (which imprints objective-dependent phases) with an evolution under a mixer Hamiltonian (which explores candidate solutions). A classical optimizer then tunes the layer angles to minimize the expected cost. The tutorial explains the building blocks, cost and mixer Hamiltonians and their layered application, and shows how PennyLane's qaoa module makes them easy to construct, then assembles a full QAOA circuit, defines the cost as an expectation value, and optimizes it with PennyLane's automatic differentiation. It applies the method to a small graph problem and recovers the optimal solution. By pairing clear explanation with PennyLane's high-level QAOA tooling, the tutorial gives an approachable, reusable foundation for variational quantum optimization in PennyLane.

Run it

pip install -r requirements.txt
python demo.py

Source and license

Imported from demonstrations_v2/tutorial_qaoa_intro/demo.py in PennyLaneAI/demos at c52c0abeb5122218aa96b38eea848864cce7323f, under the Apache License 2.0. Original authors: Xanadu and the PennyLane community. The upstream LICENSE is included alongside this example.