سومین کنفرانس بین المللی مهندسی و علوم کامپیوتر و اولین کنفرانس ملی سامانه های هوشمند، کلان داده و رایانش پیشرفته

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کد مقاله CCES-1033

عنوان Exploring Quantum Natural Language Processing for Morphologically Rich Languages: A Case Study on Persian

نویسندگان Alireza Rezaei

چکیده This work investigates QNLP for addressing the linguistic complexity of morphologically rich languages, taking Persian as a case study. By analyzing the quantum-inspired frameworks, such as the DisCoCat model and tensor-network representations using the tools Lambeq and PennyLane, it further elaborates on how superposition, entanglement, and category-theoretic structures can enhance semantic modeling, disambiguation, and syntactic analysis. Considering the rich morphology, syntactic flexibility, and restricted computational resources for Persian, QNLP offers a promising pathway toward more accurate and efficient NLP systems. The findings provide a practical blueprint for integrating quantum-inspired models with Persian-specific preprocessing and embeddings, thereby setting the stage for future experimentation on quantum hardware and diverse applications in machine translation, sentiment analysis, and parsing.

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