Enabling Quantum Natural Language Processing for Hindi Language
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Abstract (arXiv)
Quantum Natural Language Processing (QNLP) is taking huge leaps in solving the shortcomings of classical Natural Language Processing (NLP) techniques and moving towards a more "Explainable" NLP system. The current literature around QNLP focuses primarily on implementing QNLP techniques in sentences in the English language. In this paper, we propose to enable the QNLP approach to HINDI, which is the third most spoken language in South Asia. We present the process of building the parameterized quantum circuits required to undertake QNLP on Hindi sentences. We use the pregroup representation of Hindi and the DisCoCat framework to draw sentence diagrams. Later, we translate these diagrams to Parameterised Quantum Circuits based on Instantaneous Quantum Polynomial (IQP) style ansatz. Using these parameterized quantum circuits allows one to train grammar and topic-aware sentence classifiers for the Hindi Language.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 2 Dec 2023.
Builds on
6 of 17 references.
References · 6
- 2021lambeq: An Efficient High-Level Python Library for Quantum NLPcited by 43
- 2020Quantum Natural Language Processing on Near-Term Quantum Computerscited by 39
- 2014The Frobenius anatomy of word meanings I: subject and object relative pronounscited by 24
- 2020Grammar-aware sentence classification on quantum computerscited by 16
- 2021Explainable Natural Language Processing with Matrix Product Statescited by 7
- 2021Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processingcited by 2
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Citing papers per year: 2026: 2.
2 papers.