Quantum NLP for Arabic: Grammar into Circuits Outperforms Classical Models
Arabic sentences mapped to quantum circuits beat AraBERT on morphology and word sense.
Researcher Wajahath Mohammed presents the first application of quantum compositional NLP (QNLP) to Arabic—a morphologically rich, free-word-order language whose structural complexity offers a uniquely demanding testbed. Using pregroup grammar, sentences are converted into quantum circuits where subjects, verbs, and objects become quantum gates. In three controlled experiments on word order, morphological tense, and verb sense disambiguation, the quantum circuit methods were compared against classical baselines including AraVec and AraBERT.
- First quantum compositional NLP applied to Arabic, using pregroup grammar to map sentences to quantum circuits
- Beats AraVec and AraBERT on word order, morphological tense, and verb sense disambiguation tasks
- Quantum gates represent subjects, verbs, and objects; wiring topology mirrors grammatical dependencies
Why It Matters
Quantum circuits could revolutionize NLP for morphologically complex languages, offering a grammar-driven alternative to massive transformers.