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.
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.
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critical. We present the Quantum Graph Transformer (QGT), a hybrid graph-based architecture that integrates a quantum self-attention mechanism into the message-passing framework for structured language modeling. The attention mechanism is implemented using parameterized quantum circuits (PQCs), which enable the model to capture rich contextual relationships while significantly reducing the number of trainable parameters compared to classical attention mechanisms. We evaluate QGT on five sentiment classification benchmarks. Experimental results demonstrate that QGT effectively learns sentiment representations, achieving accuracies of 93.0%, 90.5%, and 88.0% on the Yelp, IMDB, and Amazon datasets, and 100% and 95.12% on the synthetic MC and RP datasets, respectively. These results highlight the potential of graph-based quantum NLP techniques for advancing efficient and scalable language understanding.
Sentiment classification remains challenging due to subtle semantic cues and long-range contextual dependencies that are often difficult for existing models to capture. Although the Quantum Graph Transformer (QGT) offers a hybrid quantum–classical perspective, its reliance on fully connected graphs and less flexible attention mechanisms limits its effectiveness. To overcome these issues, we introduce the Adaptive Quantum-Inspired Graph Transformer (AQGT), which incorporates an adaptive semantic graph construction method that forms sparse yet informative graphs, along with a quantum-inspired message passing unit for more efficient feature aggregation. AQGT processes text through a multi-layer hybrid architecture that dynamically models semantic structure and enhances node interactions through principled message passing. Experiments on multiple sentiment classification benchmarks show that AQGT achieves consistently stronger performance than classical models and the original QGT. Ablation studies validate the importance of the adaptive graph and message passing components. AQGT further displays good sample efficiency, stable behavior across hyperparameters, and strong qualitative performance in handling nuanced sentiment phenomena such as sarcasm and implicit polarity. These findings demonstrate AQGT’s capability as a robust and expressive model for complex sentiment analysis.
The data generated from non-Euclidean domains and its graphical representation (with complex-relationship object interdependence) applications has observed an exponential growth. The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms. In this study, we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data. Additionally, the quantum information theory has been applied through Graph Neural Networks (GNNs) to generate Riemannian metrics in closed-form of several graph layers. In further, to pre-process the adjacency matrix of graphs, a new formulation is established to incorporate high order proximities. The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network (GCN), particularly, the information loss and imprecise information representation with acceptable computational overhead. Moreover, the proposed Quantum Graph Convolutional Network (QGCN) has significantly strengthened the GCN on semi-supervised node classification tasks. In parallel, it expands the generalization process with a significant difference by making small random perturbations of the graph during the training process. The evaluation results are provided on three benchmark datasets, including Citeseer, Cora, and PubMed, that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.
This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
This paper introduces a novel approach to graph neural networks (GNNs) – the 'Quantum Adaptive Graph Neural Network' – leveraging quantum self-adaptation to enhance both expressiveness and computational efficiency. Traditional GNNs often struggle with the inherent complexities of graph data and require substantial computational resources. We propose a new architecture that employs quantum mechanics to simulate graph structures, allowing the network to learn more robust representations. This approach, dubbed Quantum Adaptive Graph Neural Network (QAGNN), utilizes quantum machine learning algorithms to improve the network's ability to capture intricate relationships within the graph. The paper details the core mechanism, its mathematical foundations, and preliminary experimental results demonstrating significant improvements in various graph classification tasks compared to traditional GNNs. We present a comprehensive analysis of the QAGNN's performance and discuss potential future research directions.