Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. This paper provides a controlled benchmark of embedding choices for graph classification, comparing classical baselines with quantum-oriented node representations under a unified pipeline. We evaluate two classical baselines alongside quantum-oriented alternatives, including a circuit-defined variational embedding and quantum-inspired embeddings computed via graph operators and linear-algebraic constructions. All variants are trained and tested with the same backbone, stratified splits, identical optimization and early stopping, and consistent metrics. Experiments on five different TU datasets and on QM9 converted to classification via target binning show clear dataset dependence: quantum-oriented embeddings yield the most consistent gains on structure-driven benchmarks, while social graphs with limited node attributes remain well served by classical baselines. The study highlights practical trade-offs between inductive bias, trainability, and stability under a fixed training budget, and offers a reproducible reference point for selecting quantum-oriented embeddings in graph learning.

翻译：节点嵌入作为图神经网络的信息接口，但其经验影响常在不匹配的主干网络、数据划分和训练预算下被报道。本文在统一框架下对图分类任务中的嵌入选择进行了受控基准测试，比较了经典基线方法与量子导向节点表示。我们评估了两种经典基线方法及量子导向替代方案，包括电路定义的变分嵌入、以及通过图算子和线性代数构造计算的量子启发式嵌入。所有变体均采用相同主干网络、分层数据划分、一致的优化与早停策略以及统一评价指标进行训练和测试。在五个不同TU数据集和通过目标分箱转化为分类任务的QM9数据集上的实验表明，性能存在明显的数据集依赖性：量子导向嵌入在结构驱动型基准上获得最一致的性能提升，而节点属性有限的社交图仍适合采用经典基线方法。本研究揭示了在固定训练预算下归纳偏置、可训练性与稳定性之间的实际权衡，并为在图学习中选择量子导向嵌入提供了可复现的参考基准。