将长文本高效转为量子电路，保留语法和语义关系
Efficient Generation of Parameterised Quantum Circuits from Large Texts
- 用树状预群图结构编码文本语法关系
- 实测可处理高达6410词的复杂长文
- 适合量子自然语言处理研究者使用
量子自然语言处理正在重新定义语言信息的表示与处理方式。传统混合量子-经典模型高度依赖经典神经网络，而近期提出的DisCoCirc框架能直接将完整文档编码为参数化量子电路（PQCs），兼具可解释性和组合性优势。本文提出一种高效方法，利用预群图的树状表示，将大规模文本转化为量子电路。基于语言与量子力学在对称单子范畴中的组合类比，该方法能忠实且高效地编码长篇复杂文本中的句法与语篇关系（实验中最大达6410词）。所开发系统已作为增强版开源量子NLP工具包lambeq Gen II的一部分发布。
原文摘要 · Abstract (English)
Quantum approaches to natural language processing (NLP) are redefining how linguistic information is represented and processed. While traditional hybrid quantum-classical models rely heavily on classical neural networks, recent advancements propose a novel framework, DisCoCirc, capable of directly encoding entire documents as parameterised quantum circuits (PQCs), besides enjoying some additional interpretability and compositionality benefits. Following these ideas, this paper introduces an efficient methodology for converting large-scale texts into quantum circuits using tree-like representations of pregroup diagrams. Exploiting the compositional parallels between language and quantum mechanics, grounded in symmetric monoidal categories, our approach enables faithful and efficient encoding of syntactic and discourse relationships in long and complex texts (up to 6410 words in our experiments) to quantum circuits. The developed system is provided to the community as part of the augmented open-source quantum NLP package lambeq Gen II.
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