Quantum-Enhanced Adaptive Graph Convolutional Networks for Sentiment Representation Learning

Sentiment classification struggles with complex semantic relationships using static text graphs. We introduce the Quantum-Enhanced Adaptive Graph Convolutional Network (QAGCN), a hybrid quantum-classical architecture for robust sentiment representation. QAGCN's core is a Quantum-Enhanced Graph Construction Module employing a Parameterized Quantum Circuit (PQC) to dynamically learn emotional association strengths between word pairs. This generates a task-adaptive adjacency matrix, which then feeds into classical GNN layers. Evaluations on benchmark datasets (Yelp, IMDB, Amazon, MC, RP) demonstrate QAGCN's superior or competitive accuracy against state-of-the-art classical graph models and the Quantum Graph Transformer. QAGCN notably improved performance on Amazon where prior quantum models struggled, underscoring its adaptive graph construction's efficacy. An ablation study confirms the critical contribution of PQC-driven adaptive graph learning. Our findings highlight the significant potential of quantum-enhanced adaptive graph learning for complex Natural Language Processing.