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arXiv:2508.21332

Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures

Chi-Sheng Chen, En-Jui Kuo

Published Aug 29, 2025• 0 citations
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Abstract

This paper presents a comprehensive evaluation of quantum text generation models against traditional Transformer/MLP architectures, addressing the growing interest in quantum computing applications for natural language processing. We conduct systematic experiments comparing five distinct models: Transformer (baseline), Quantum Kernel Self-Attention Network (QKSAN), Quantum RWKV (QRWKV), and Quantum Attention Sequence Architecture (QASA) across five diverse datasets including simple sentences, short stories, quantum phrases, haiku poetry, and proverbs. Our evaluation employs multiple metrics including perplexity, BLEU scores, vocabulary diversity, repetition rates, and fluency measures to assess different aspects of text generation quality. The experimental results reveal that while traditional Transformer models maintain overall superiority with the lowest average perplexity (1.21) and highest BLEU-1 score (0.2895), quantum-inspired models demonstrate competitive performance in specific scenarios. Notably, QKSAN achieves a competitive BLEU-1 score of 0.2800 while maintaining zero repetition rates, and QRWKV demonstrates perfect vocabulary diversity (Distinct-1 = 1.000) in certain tasks.

DOI
10.48550/arxiv.2508.21332

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This paper presents a hybrid quantum-classical language model for text generation, evaluated through a detailed case study of O. Henry's classic short story, The Gift of the Magi. The model integrates a classical transformer with a quantum generator to enhance the extraction of abstract narrative features. Our system architecture includes stages for input tokenization, contextual embedding through multi-head attention, quantumenhanced feature extraction using a parameterized quantum circuit, and output refinement through a classical discriminator. Quantitative evaluations using the BLEU score, besides qualitative analysis, reveal that while the hybrid approach generates syntactically coherent text, it currently falls short in capturing the nuanced symbolic and emotional depth of the original narrative. The findings show the present limitations of quantum-enhanced language models and point towards future research directions in refining quantum circuit designs, reducing noise, and improving integration strategies to fully exploit the potential of quantum computing in natural language processing.
Recent advances in Natural Language Processing have been predominantly driven by transformer-based architectures, which rely heavily on self-attention mechanisms to model relationships between tokens in a sequence. Similarly, the field of Quantum Natural Language Processing, which seeks to leverage quantum principles to address challenges in language understanding and generation tasks, has seen the recent development of quantum self-attention mechanisms. We propose a classical quantum-inspired self-attention (QISA) mechanism and integrate it into the full autoregressive language modeling pipeline of GPT-1. To the best of our knowledge, this is the first integration of this kind, as previous quantum self-attention mechanisms have been primarily tested on text classification. In our experiments, QISA achieves better performance when compared to standard self-attention on the metrics character error rate (15.5×15.5\times15.5× better), word error rate ($4.7 \times $) and cross-entropy loss (13×13 \times13×). This is achieved while only requiring a $ 2.6\times$ longer inference time.
Quantum computing, with its unique principles such as superposition and entanglement, promises to transform various domains, including artificial intelligence. This study investigates the fusion of quantum computing with contemporary AI technologies through quantum transfer learning. We introduce the Complex Quantum Kernel Self-Attention Network (CQKSAN), a novel model that leverages quantum circuits and self-attention mechanisms to boost text classification performance. By applying quantum transfer learning, CQKSAN harnesses the strengths of pre-trained classical models and enhances them with quantum capabilities. Experimental results on multiple binary classification datasets demonstrate that CQKSAN achieves a more efficient learning process. Specifically, it improves training accuracy and validation accuracy by up to 24.31% and 28.35%, respectively, and increases the Matthews Correlation Coefficient by 113.10%. Moreover, CQKSAN consistently outperforms the BERT baseline in both training and validation accuracy across various datasets, showcasing its strong learning capability and generalization performance in handling tasks with diverse linguistic structures and complex semantics. Our findings underscore the potential of hybrid quantum-classical approaches, while also identifying the current limitations and paving the way for future research in fully quantum-based learning systems.
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This article proposes a quantum self-attention neural network (QSAN) model that is implementable on parameterized quantum circuits (PQCs), providing a novel avenue for addressing natural language processing (NLP) tasks via quantum computing. The QSAN architecture comprises four blocks: the data preprocessing block, the quantum encoding block, the model design block, and the network optimization block. Through these blocks, classical text data are initially preprocessed and encoded into quantum states. The rich semantic features and intricate relationships within these quantum states are then captured and learned by a quantum self-attention layer and a quantum fully connected layer. Finally, the model performance is enhanced by optimizing the network parameters. Simulation results on publicly available topic classification and sentiment analysis datasets demonstrate that the QSAN model outperforms state-of-the-art baselines. As a limitation of the current study, these evaluations are restricted to a maximum sentence length of eight words due to simulation and hardware constraints. Moreover, the effectiveness, scalability, trainability, and robustness of the proposed model are comprehensively evaluated, highlighting its superior performance and potential for advanced NLP tasks.
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