The arXiv preprint 2605.27410 introduces MZeQAS, which replaces repeated full training of candidate variational circuits with a zero-shot proxy based on convergence of the Quantum Neural Tangent Kernel Gram matrix. This Monte Carlo Tree Search framework evaluates expressivity and trainability without gradient descent iterations, directly targeting the barren-plateau and hardware-mapping bottlenecks that have constrained variational quantum algorithms since the 2018-2020 wave of VQA proposals. Unlike earlier evolutionary quantum neural architecture search methods that expend compute on each candidate, MZeQAS achieves reported gains in both search speed and final solution quality on noisy intermediate-scale devices. The work remains a preprint without peer review or disclosed benchmark sample sizes, leaving open questions about statistical robustness across random seeds and device noise models. Related classical zero-shot neural architecture search literature (e.g., Chen et al., 2021 on proxy metrics) shows similar speed-ups can degrade when kernel assumptions fail under distribution shift; analogous risks apply here if QNTK convergence does not generalize to deeper or more entangled ansatze. A second connection appears in studies of quantum kernel methods (Schuld & Killoran, 2019), where Gram-matrix stability was already flagged as sensitive to circuit depth, a factor MZeQAS exploits but does not fully stress-test. If the surrogate holds, the technique supplies the missing automation layer that could move quantum machine learning hardware design from artisanal tuning to systematic search, compressing iteration cycles that currently limit practical deployment.