AI BriefingSolving the Max-Cut Problem with PennyLane-Based QAOA: Amazon Braket Hybrid Jobs Implementation Guide (Part 1)
·2026.10.23 19:24
Key point
This article explains the code structure and principles of the maxcut_qaoa module for solving the Max-Cut problem using QAOA, step by step.
Details
- The objective function of the Max-Cut problem is transformed into a cost Hamiltonian, redefining it as an energy minimization problem
- The QAOA circuit repeats the cost (ZZ) and mixer (RX) Hamiltonians for the number of layers (p), with parameters γ and β adjusted by a classical optimizer
- PennyLane's GradientDescentOptimizer is used to optimize parameters in the direction that lowers the expected value of the cost function
- The get_device_instance function allows switching between a local simulator, the Braket on-demand simulator, and a QPU with a single ARN line
- Normal operation was confirmed in an environment with PennyLane 0.45.1 and Amazon Braket SDK 1.127.0
- Part 2 will compare the differences between three execution methods: local, embedded, and on-demand (SV1)
