This study explores the impact of pretraining Graph Transformers using atom-level quantum-mechanical features for molecular property modeling. We utilize the ADMET Therapeutic Data Commons datasets to evaluate the benefits of this approach. Our results show that pretraining on quantum atomic properties improves the performance of the Graphormer model. We conduct comparisons with two other pretraining strategies: one based on molecular quantum properties (specifically the HOMO-LUMO gap) and another using a self-supervised atom masking technique. Additionally, we employ a spectral analysis of Attention Rollout matrices to understand the underlying reasons for these performance enhancements. Our findings suggest that models pretrained on atom-level quantum mechanics are better at capturing low-frequency Laplacian eigenmodes from the molecular graphs, which correlates with improved outcomes on most evaluated downstream tasks, as measured by our custom metric.