# Enabling Quantum Natural Language Processing for Hindi Language

arXiv: https://arxiv.org/abs/2312.01221 · HTML: https://mlsanity.com/paper/2312.01221/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [lambeq: An Efficient High-Level Python Library for Quantum NLP](https://mlsanity.com/paper/2110.04236/) (2021)
- [Quantum Natural Language Processing on Near-Term Quantum Computers](https://mlsanity.com/paper/2005.04147/) (2020)
- [The Frobenius anatomy of word meanings I: subject and object relative pronouns](https://mlsanity.com/paper/1404.5278/) (2014)
- [Grammar-aware sentence classification on quantum computers](https://mlsanity.com/paper/2012.03756/) (2020)
- [Explainable Natural Language Processing with Matrix Product States](https://mlsanity.com/paper/2112.08628/) (2021)
- [Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processing](https://mlsanity.com/paper/2102.02204/) (2021)

## Built on by (most cited first, 2 of 2)

- [Quantum Compositional NLP for Arabic: Grammar, Morphology, and Word Sense in Circuit Topology](https://mlsanity.com/paper/2607.14100/) (2026)
- [Scaling Hindi Quantum Natural Language Processing through Automatic Pregroup Supertagging](https://mlsanity.com/paper/2609.13721/) (2026)

## 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.