Structural Bottlenecks in QAOA Mixer Design: Transport and Causal Reach

The choice of mixer is a central design decision in the Quantum Approximate Optimization Algorithm (QAOA), yet different structural limitations of a mixer need not be captured by a single metric. We identify two complementary structural bottlenecks for shallow QAOA: configuration-space transport, which limits how strongly probability can concentrate on high-quality solutions, and finite-depth causal reach, which limits which parts of a problem instance can influence a local objective term. In Part I, we derive first-order mixer-dependent objective bounds in complementary small-$β$ and small-$γ$ regimes. For connected regular mixer graphs, the two leading-order expressions are exactly equivalent through a graph-Laplacian identity and reduce to the same cost-weighted Dirichlet energy. We then derive a non-perturbative upper envelope from the layer-wise ₁ spreading of the mixer unitary, yielding a cost-distribution-dependent necessary spreading condition for attaining a target approximation ratio. In Part II, we study the locality bottleneck for MaxCut by augmenting the transverse-field mixer with problem-graph-aligned two-local interactions. The resulting k-hop construction has causal radius at most $(k+1)p$ at depth p; for the $k=1$ architectures studied numerically, this gives radius $2p$. Across cycles, circular ladders, and high-girth regular bipartite graphs, X+XX and X+YY improve the best-found approximation ratios over the standard X mixer at equal depth, while X+YY at depth p comes within about one approximation-ratio point of plain X at depth $2p$ in the reach-matched cases examined. The unequal performance of the equal-reach $XX$ and $YY$ augmentations shows that satisfying a structural capability does not by itself guarantee performance. Together, the results provide complementary structural insights for mixer design.