On the dynamical Lie algebras of quantum approximate optimization algorithms
Jonathan Allcock, Miklos Santha, Pei Yuan, Shengyu Zhang

TL;DR
This paper analytically studies the dynamical Lie algebras of QAOA, providing bounds and explicit bases for specific graphs, and demonstrating the absence of barren plateaus in certain cases, advancing understanding of QAOA's expressiveness and trainability.
Contribution
It offers the first analytical characterization of DLAs for QAOA on specific graphs, including explicit bases and variance formulas, improving theoretical understanding.
Findings
Explicit basis for cycle graph DLA and its decomposition.
Proof of absence of barren plateaus for cycle graphs.
Dimension bounds and explicit bases for complete graph DLAs.
Abstract
Dynamical Lie algebras (DLAs) have emerged as a valuable tool in the study of parameterized quantum circuits, helping to characterize both their expressiveness and trainability. In particular, the absence or presence of barren plateaus (BPs) -- flat regions in parameter space that prevent the efficient training of variational quantum algorithms -- has recently been shown to be intimately related to quantities derived from the associated DLA. In this work, we investigate DLAs for the quantum approximate optimization algorithm (QAOA), one of the most studied variational quantum algorithms for solving graph MaxCut and other combinatorial optimization problems. While DLAs for QAOA circuits have been studied before, existing results have either been based on numerical evidence, or else correspond to DLA generators specifically chosen to be universal for quantum computation on a subspace of…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture
