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Comparing Local, Embedded, and On-Demand Execution of PennyLane-Based QAOA

·2026.11.06 23:36

Key point

We ran QAOA code in three environments to verify cost convergence and differences in billing structures.

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Details

  • Executed and compared QAOA code implemented with PennyLane across three methods: local, embedded, and on-demand simulators
  • Changing only the device ARN alters the location of quantum computation, which directly impacts billing methods and network latency characteristics
  • The embedded simulator bills only for instance time, whereas on-demand (SV1) incurs additional costs per task submission and network round trips
  • After 40 iterations, the cost converged to approximately -4.1, and the top probability partition (11100) found the theoretical maximum cut (5)
  • Local execution is useful for debugging and establishing baselines, but it has the limitation of not leaving records in the Braket console
  • When using an actual QPU, the same code structure is maintained, and queue wait times are shortened via priority access after job start
amazon-braketpennylaneqaoamax-cuthybrid-jobsquantum-simulatorsv1
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