Dynamic link prediction is important for modeling evolving interactions in social, communication, financial, and transportation networks. Classical temporal graph models capture changes over time, but they may struggle to represent rapidly evolving node-edge interactions in large dynamic graphs. We propose A2QTGN (Adaptive Amplitude Quantum-Integrated Temporal Graph Network), a hybrid quantum-classical framework that introduces adaptive amplitude encoding as a temporal embedding layer within a Temporal Graph Network. Unlike fixed quantum embeddings, the proposed module maps temporally varying node features into quantum states and selectively refreshes their amplitude representations according to the magnitude of feature change. This allows the framework to preserve stable node information, emphasize meaningful temporal variations, and reduce redundant quantum re-encoding. Across five Temporal Graph Benchmark datasets, A2QTGN achieves test area under the curve (AUC) values of up to 0.9957 and mean reciprocal rank (MRR) values of up to 0.7832, including the highest MRR among the compared baselines on tgbl-review and tgbl-flight. Ablation results further show that the adaptive quantum embedding is central to the model performance: on a 25k-event subset of tgbl-wiki, it improves test accuracy by 13.36 percentage points over always updating and by 22.44 percentage points over using no updates. The trained model is also evaluated using noisy simulations based on an IBM quantum device, followed by a smaller real-device experiment. The results show that adaptive quantum embeddings can provide effective temporal representations for dynamic link prediction while remaining executable under current quantum hardware constraints.

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