Quantum Compositional NLP for Arabic: Grammar, Morphology, and Word Sense in Circuit Topology

阿拉伯语量子组合自然语言处理：电路拓扑中的语法、形态学与词义

Abstract: We present the first application of pregroup grammar-based quantum compositional natural language processing (QNLP) to Arabic; a morphologically rich, free-word-order language whose structural complexity provides a uniquely demanding testbed for theories of meaning composition in quantum circuits.

摘要： 我们首次将基于前群语法（pregroup grammar）的量子组合自然语言处理（QNLP）应用于阿拉伯语。阿拉伯语是一种形态丰富且词序自由的语言，其结构复杂性为量子电路中的意义组合理论提供了一个极具挑战性的测试平台。

Our system converts Arabic sentences into quantum circuits whose topology mirrors grammatical structure: subjects, verbs, and objects become quantum gates, and the typed dependencies between them (the pregroup grammar) determine how those gates are wired together.

我们的系统将阿拉伯语句子转换为量子电路，其拓扑结构反映了语法结构：主语、谓语和宾语转化为量子门，而它们之间的类型依赖关系（前群语法）则决定了这些量子门的连接方式。

We conduct three controlled experiments spanning word order, morphological tense, and verb sense disambiguation, comparing quantum circuit methods against classical baselines including AraVec (Arabic word embeddings) and AraBERT (a pre-trained Arabic transformer).

我们进行了三项对照实验，涵盖了词序、形态时态和动词词义消歧，并将量子电路方法与包括 AraVec（阿拉伯语词嵌入）和 AraBERT（预训练阿拉伯语 Transformer 模型）在内的经典基准方法进行了比较。