  We use a GPU-accelerated version of the massively parallel J\"ulich universal
quantum computer simulator (JUQCS--G) to benchmark JUWELS Booster, a GPU
cluster with 3744 NVIDIA A100 Tensor Core GPUs. Using JUQCS--G, we study the
relation between quantum annealing (QA) and the quantum approximate
optimization algorithm (QAOA). We find that a very coarsely discretized version
of QA, termed approximate QA, performs surprisingly well in comparison to the
QAOA. It can either be used to initialize the QAOA, or to avoid the costly
optimization procedure altogether. Furthermore, we study the scaling of
approximate QA for exact cover problems from 30 to 40 qubits. We find that the
case with largest discretization error performs most favorably, surpassing the
best result obtained from the QAOA.
