ABSTRACT Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT) , a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self‐attention. The QGT models variable‐length sentences as token graphs, where both the embedding encoding and the self‐attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and ‐nearest‐neighbor graph structures and evaluate it on five benchmark sentiment‐classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving fewer parameters while requiring 3– fewer samples to reach comparable performance. These findings highlight the potential of graph‐based quantum models as scalable and data‐efficient architectures for natural language understanding.
ABSTRACT Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT) , a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self‐attention. The QGT models variable‐length sentences as token graphs, where both the embedding encoding and the self‐attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and ‐nearest‐neighbor graph structures and evaluate it on five benchmark sentiment‐classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving fewer parameters while requiring 3– fewer samples to reach comparable performance. These findings highlight the potential of graph‐based quantum models as scalable and data‐efficient architectures for natural language understanding.
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.
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.
Recent advances in quantum computing have opened new pathways for enhancing deep learning architectures, particularly in domains characterized by high-dimensional and context-rich data such as natural language processing (NLP). In this work, we present a hybrid classical-quantum Transformer model that integrates a quantum-enhanced attention mechanism into the standard classical architecture. By embedding token representations into a quantum Hilbert space via parameterized variational circuits and exploiting entanglement-aware kernel similarities, the model captures complex semantic relationships beyond the reach of conventional dot-product attention. We demonstrate the effectiveness of this approach across diverse NLP benchmarks, showing improvements in both efficiency and representational capacity. The results section reveal that the quantum attention layer yields globally coherent attention maps and more separable latent features, while requiring comparatively fewer parameters than classical counterparts. These findings highlight the potential of quantum-classical hybrid models to serve as a powerful and resource-efficient alternative to existing attention mechanisms in NLP.
Abstract The combination of Transformer architectures with Quantum Computing has opened new directions in Quantum Natural Language Processing (QNLP) by leveraging the Transformer’s representational capacity together with the computational parallelism and high-dimensional feature space of quantum systems. However, existing approaches remain constrained by partial designs and limited integration, hindering scalability and trainability. This study introduces FitQT—a novel, fully integrated, and trainable quantum transformer that unifies three essential components: (1) a quantum multi-head attention mechanism employing a scaled Gaussian kernel, (2) a quantum feed-forward network, and (3) quantum residual connections. The residual connections stabilize gradient flow and facilitate the training of deeper quantum circuits, while the attention and feed-forward components jointly enhance representational capacity. Evaluations on sentiment analysis datasets demonstrate FitQT’s effectiveness. On the IMDb dataset, FitQT improves accuracy by 14.89% over the baseline Quantum Self-Attention Network and achieves competitive task-level performance relative to BERT-Base, despite being trained from scratch without large-scale pretraining, while requiring only 2,595 parameters and substantially reduced training time. Moreover, FitQT achieves state-of-the-art performance compared to prior quantum-based transformer architectures such as QSANN and QMSAN. These results highlight the importance of incorporating classical deep learning principles into quantum architectures and provide a practical foundation for scalable, resource-efficient QNLP systems suitable for the noisy intermediate-scale quantum era.
The success of the self-attention mechanism in classical machine learning models has inspired the development of quantum analogs aimed at reducing the computational overhead. Self-attention integrates learnable query and key matrices to calculate attention scores between all pairs of tokens in a sequence. These scores are then multiplied by a learnable value matrix to obtain the output self-attention matrix, enabling the model to effectively capture long-range dependencies within the input sequence. Here, we propose a hybrid quantum-classical self-attention mechanism as part of a transformer decoder, the architecture underlying large language models (LLMs). To demonstrate its utility in chemistry, we train this model on the QM9 dataset for conditional generation, using SMILES strings as input, each labeled with a set of physicochemical properties that serve as conditions during inference. Our theoretical analysis shows that the time complexity of the query-key dot product is reduced from O ( n 2 d ) in a classical model to O ( n 2 log d ) in our quantum model, where n and d represent the sequence length and the embedding dimension, respectively. We perform simulations using NVIDIA’s CUDA-Q platform, which is designed for efficient GPU scalability. This work provides a promising avenue for quantum-enhanced natural language processing (NLP).