Analysis shows GM-QAOA with multiple layers avoids barren plateaus, indicating improved efficiency in quantum optimization.
We analyze the dynamical Lie algebras (DLAs) associated with the Grover-mixer variant of the Quantum Approximate Optimization Algorithm (GM-QAOA). When the initial state is the uniform superposition of computational basis states, we show that the corresponding DLA is isomorphic to either suᵣ ⊕ u₁⊕ 2 or suᵣ ⊕ u₁, where \(r\) denotes the number of distinct values of the objective function. We also establish an analogous classification for other choices of initial states and Grover-type mixers. Furthermore, we prove that the DLA of GM-QAOA has the largest possible commutant among all QAOA variants initialized with the same state |ξ, corresponding physically to the maximal set of conserved quantities. In addition, we derive an explicit formula for the variance of the GM-QAOA loss function in terms of the objective function values, and we show that, for a broad class of optimization problems, GM-QAOA with sufficiently many layers avoids barren plateaus.
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Tsvelikhovskiy et al. (2025) studied this question.
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