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

Researchers have applied quantum compositional natural language processing (QNLP) to Arabic for the first time, converting sentences into quantum circuits that mirror grammatical structure. The system handles Arabic's complex morphology and free word order, outperforming classical baselines like AraVec and AraBERT in experiments on word order, tense, and verb sense disambiguation.

arXiv:2607.14100v1 Announce Type: new 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. 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. 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 .