Graph neural networks (GNNs) have demonstrated strong capabilities in graph representation learning but still face limitations in efficiency and scalability. Quantum GNNs (QGNNs) offer a promising alternative. However, existing approaches often fail to fully exploit edge information, require substantial quantum resources, and insufficiently account for permutation invariance in graph learning. To address these challenges, this article proposes a permutation-invariant quantum GNN (PIQGNN). The proposed model introduces a low-qubit-cost quantum encoding strategy that jointly embeds node features, edge features, and graph topology into entangled quantum states using only $n$ qubits, where $n$ denotes the number of nodes, while explicitly enforcing permutation invariance. Furthermore, a symmetry-aware variational quantum neural network (QNN) is designed to enable end-to-end permutation-invariant learning. Its hyperparameters are optimized via Bayesian optimization to alleviate barren plateau (BP) effects and enhance training stability. Experimental results on multiple graph binary classification benchmark datasets demonstrate that, compared with classical GNNs, PIQGNN achieves competitive performance with a significantly reduced number of trainable parameters. Compared with existing QGNNs, PIQGNN attains higher accuracy with lower quantum resource requirements and exhibits stronger robustness under noisy conditions. These results indicate that PIQGNN provides an efficient, scalable, and noise-resilient quantum framework for graph learning, highlighting its practical potential in the noisy intermediate-scale quantum (NISQ) era.
Maoduo Li, Wen Liu, Lei Shi et al.· IEEE Transactions on Neural...· 0 citations
Hashing algorithms represent data by generating compact binary hash codes, enabling efficient cross-modal similarity search and significantly improving the storage efficiency and retrieval performance of image-text data. However, because traditional hashing methods typically separate image-text feature extraction from hash learning, the feature extraction module struggles to adaptively update based on training feedback, further limiting the performance of cross-modal retrieval in real-world scenarios. To address this issue, deep learning has been introduced to cross-modal hashing, enabling end-to-end joint optimization and tightly integrating feature extraction and hash learning, significantly improving retrieval performance. However, because existing deep learning methods often use a fixed weight distribution when processing samples, they ignore the modal differences between text and visual features when fusing them. This leads to an inadequate fused representation and difficulty achieving optimal modality alignment during hash code generation. To address this issue, we propose a Dual Graph Network Hashing (DGNH) algorithm that dynamically adjusts the weight distribution between visual and text features through an adaptive attention mechanism, ensuring better modality fusion during hash code generation. Specifically, we design a novel framework that combines a graph convolutional neural network (GCN) with a graph attention network (GAT) to construct a label classifier for generating labels and enhancing cross-modal feature representation. This approach improves feature discrimination by capturing the hierarchical relationships and co-occurrence patterns of labels through a carefully constructed label association graph. Furthermore, we introduced a pre-trained model combining CLIP and the Transformer to further enhance the overall feature representation. During the optimization phase, we employed a contrastive triplet loss function coupled with novel regularization constraints for quantization and optimization, thus effectively reducing information loss during discretization and ensuring the generated hash codes are more compact and efficient. Experimental results on three public datasets, MS-COCO, NUS-WIDE, and MIRFlickr-25 K, demonstrate that the proposed method outperforms existing methods in both retrieval accuracy and efficiency, thereby validating its effectiveness and superiority.
Shuang Zhang, Yue Wu, Lei Shi et al.· IEEE Transactions on Knowled...· 0 citations
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