Enabling Quantum Natural Language Processing for Hindi Language

Naman Srivastava, Gaurang Belekar, Sunil Saumya, Aswath Babu H
12/2/2023
cs.CL

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Abstract

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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Cite this paper

@article{srivastava2023enabling,
  title  = {Enabling Quantum Natural Language Processing for Hindi Language},
  author = {Naman Srivastava and Gaurang Belekar and Sunil Saumya and Aswath Babu H},
  year   = {2023},
  eprint = {2312.01221},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.01221v1}
}

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