# A gentle introduction to Quantum Natural Language Processing

arXiv: https://arxiv.org/abs/2202.11766 · HTML: https://mlsanity.com/paper/2202.11766/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [lambeq: An Efficient High-Level Python Library for Quantum NLP](https://mlsanity.com/paper/2110.04236/) (2021)
- [Quantum Algorithms for Compositional Natural Language Processing](https://mlsanity.com/paper/1608.01406/) (2016)
- [The Frobenius anatomy of word meanings II: possessive relative pronouns](https://mlsanity.com/paper/1406.4690/) (2014)
- [Towards logical negation for compositional distributional semantics](https://mlsanity.com/paper/2005.04929/) (2020)
- [Conversational Negation using Worldly Context in Compositional Distributional Semantics](https://mlsanity.com/paper/2105.05748/) (2021)
- [A hybrid classical-quantum workflow for natural language processing](https://mlsanity.com/paper/2004.06800/) (2020)

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

- [Toward Quantum Machine Translation of Syntactically Distinct Languages](https://mlsanity.com/paper/2307.16576/) (2023)
- [Variational Quantum Classifiers for Natural-Language Text](https://mlsanity.com/paper/2303.02469/) (2023)

## Abstract (arXiv)

The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.