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Enabling Quantum Natural Language Processing for Hindi Language

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arxiv 2312.01221 v1 pith:CLEHNMC6 submitted 2023-12-02 cs.CL

Enabling Quantum Natural Language Processing for Hindi Language

Naman Srivastava , Gaurang Belekar , Sunil Saumya , Aswath Babu H This is my paper
classification cs.CL
keywords languagequantumhindiqnlpcircuitsnaturalprocessingdiagrams
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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