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From Trainability Diagnostics to Optimization Claims: Boundaries and Controls in Variational Quantum Optimization
Barren plateau diagnostics characterize whether gradient signal remains available for training, but surviving signal need not translate into successful optimization. We study this trainability--optimization gap at the level of optimizer steps. Treating coefficient-weighted Hamiltonian-term gradients as task-like components, we introduce step-level diagnostics and derive an exact bridge between signed termwise organization, directional activity, and first-order descent. Resolving this bridge into standard first-order geometry shows that the apparent organization--activity factors are not indepe
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- arXiv · AI, language, vision and robotics · 2026-09-18T02:36:42.000Z
First collected: 2026-09-23T14:01:59.594Z. This is not the publication date.