  The parity mapping provides a geometrically local encoding of the Quantum
Approximate Optimization Algorithm (QAOA), at the expense of having a quadratic
qubit overhead for all-to-all connected problems. In this work, we benchmark
the parity-encoded QAOA on spin-glass models. We address open questions in the
scaling of this algorithm, and show that for fixed number of parity-encoded
QAOA layers, the performance drops as $N^{-1/2}$. We perform tensor-network
calculations to confirm this result, and comment on the concentration of
optimal QAOA parameters over problem instances.
