量子模型在特定任务中表现不俗，可零重复生成文本
Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures
- 融合量子与经典架构，设计多模型对比框架
- QKSAN BLEU-1达0.2800且无重复，QRWKV词汇多样性满分
- 适合关注量子计算与NLP交叉应用的研究者
本文系统评估了量子文本生成模型与传统Transformer/MLP架构的性能差异，回应自然语言处理中量子计算应用日益增长的兴趣。我们在五个数据集（简单句、短篇故事、量子短语、俳句、谚语）上对比五种模型：Transformer（基线）、量子核自注意力网络（QKSAN）、量子RWKV（QRWKV）和量子注意力序列架构（QASA）。采用困惑度、BLEU分数、词汇多样性、重复率和流畅性等多指标评估生成质量。结果表明，尽管传统Transformer整体最优（平均困惑度1.21，BLEU-1 0.2895），但量子模型在特定场景表现优异：QKSAN实现0.2800的BLEU-1且重复率为零；QRWKV在某些任务中达到词汇多样性满分（Distinct-1 = 1.000）。
原文摘要 · Abstract (English)
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.
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