Quantum Graph Transformer for NLP Sentiment Classification (2506.07937v1)

Abstract: 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 LLMing. 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 show that QGT consistently achieves higher or comparable accuracy than existing quantum natural language processing (QNLP) models, including both attention-based and non-attention-based approaches. When compared with an equivalent classical graph transformer, QGT yields an average accuracy improvement of 5.42% on real-world datasets and 4.76% on synthetic datasets. Additionally, QGT demonstrates improved sample efficiency, requiring nearly 50% fewer labeled samples to reach comparable performance on the Yelp dataset. These results highlight the potential of graph-based QNLP techniques for advancing efficient and scalable language understanding.

• The paper introduces a hybrid architecture combining quantum self-attention and graph neural networks to reduce parameter complexity.
• The QGT model achieves superior accuracy, demonstrating an average gain of 5.42% on real-world benchmarks while using 50% fewer labeled samples.
• The study paves the way for scalable, efficient NLP frameworks by leveraging quantum properties to represent complex token relationships.

Quantum Graph Transformer for NLP Sentiment Classification: A Deep Dive

The Quantum Graph Transformer (QGT) proposed by Aktar et al. introduces a quantum-enhanced approach to sentiment classification within the domain of NLP. By integrating quantum principles with graph-based message processing, the QGT model aims to address inherent limitations of classical NLP models such as high parameter complexity and extensive data requirements.

Architecture Overview

The QGT model represents a hybrid architecture merging quantum self-attention and graph neural network techniques. The principal innovation lies in the Quantum Transformer Convolution (QTransformerConv) layer, where Parameterized Quantum Circuits (PQCs) are deployed to compute self-attention scores within a fully connected graph structure representing each sentence. This framework significantly reduces the dimensionality of trainable parameters compared to classical attention-based approaches, utilizing quantum circuits to encode and propagate token information across sentences.

Performance Benchmarks

Experimental evaluations of the QGT model were conducted across five sentiment classification benchmarks: three real-world datasets (Yelp, IMDB, Amazon) and two synthetic datasets (MC and RP). Empirical results indicate QGT consistently achieves superior accuracy compared to existing quantum NLP models, including those leveraging attention-based mechanisms, as well as classical graph transformers. Notably, QGT demonstrates an average accuracy gain of 5.42% on real-world datasets and 4.76% on synthetic datasets when compared with equivalent classical models.

Furthermore, QGT showcases enhanced sample efficiency, requiring approximately 50% fewer labeled samples in the Yelp dataset to reach equivalent performance levels as classical counterparts. This improvement highlights the potential of quantum-based architectures for mitigating data dependency and computational resource limitations prevalent in traditional NLP methodologies.

Theoretical and Practical Implications

The primary theoretical contribution of QGT resides in its novel fusion of quantum computing and graph neural networks within NLP sentiment classification. By leveraging quantum states for encoding and processing token information, the architecture harnesses quantum properties like superposition and entanglement to explore complex semantic relationships more effectively. Practically, this advancement promises enhanced model scalability and efficiency, particularly in data-constrained contexts.

Future Directions

Looking ahead, further exploration into multi-layered and multi-headed quantum self-attention architectures could yield richer semantic representations for complex NLP classification tasks. Larger datasets such as SST-5 or IMDb Review datasets could provide an avenue for comprehensive evaluations of QGT's scalability and generalization capabilities. In addition, investigating optimization strategies for PQCs under noisy conditions might reveal robust methodologies for practical quantum NLP deployment.

In conclusion, the Quantum Graph Transformer presents a compelling step forward in integrating quantum computing principles within the structured LLMing field. Its potential implications across NLP tasks offer promising directions for advancing efficient, scalable language understanding frameworks.