# Scaling Hindi Quantum Natural Language Processing through Automatic Pregroup Supertagging

arXiv: https://arxiv.org/abs/2609.13721 · HTML: https://mlsanity.com/paper/2609.13721/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [Foundations for Near-Term Quantum Natural Language Processing](https://mlsanity.com/paper/2012.03755/) (2020)
- [AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages](https://mlsanity.com/paper/2005.00085/) (2020)
- [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)
- [A* CCG Parsing with a Supertag and Dependency Factored Model](https://mlsanity.com/paper/1704.06936/) (2017)
- [Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks](https://mlsanity.com/paper/2010.06115/) (2020)
- [HindiLLM: Large Language Model for Hindi](https://mlsanity.com/paper/2412.20357/) (2024)
- [Enabling Quantum Natural Language Processing for Hindi Language](https://mlsanity.com/paper/2312.01221/) (2023)

## Abstract (arXiv)

Quantum Natural Language Processing (QNLP) uses pregroup grammars to translate grammatical structure into diagrammatic representations and quantum circuits. Recent Hindi QNLP work has shown that Hindi-specific pregroup grammars can support grammar-sensitive compositional models, but grammatical type assignment is still largely manual, limiting scalability. This paper formulates automatic Hindi pregroup supertagging as a token-level classification task. Using a manually annotated corpus of 380 Hindi sentences, we evaluate lexical, contextual, prompting-based, lexical-repair, and suffix/morphology-aware methods. Results show that simple lexical and contextual models are strong in this low-resource setting: contextual backoff achieves the best completed accuracy of 64.56%, while raw Qwen2.5 prompting reaches only 11.65%. Lexical repair raises LLM-assisted prediction to 64.08%, demonstrating the value of constraining generative outputs with symbolic grammar knowledge. Diagnostic analysis further shows that seen and unambiguous tokens are much easier than unseen tokens, and suffix/morphology features improve karaka-token accuracy but not overall performance. These results show that automatic Hindi pregroup assignment is feasible and can reduce reliance on manual annotation in future multilingual QNLP pipelines.