# Quantum Natural Language Processing on Near-Term Quantum Computers

arXiv: https://arxiv.org/abs/2005.04147 · HTML: https://mlsanity.com/paper/2005.04147/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 30 of 39)

- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [lambeq: An Efficient High-Level Python Library for Quantum NLP](https://mlsanity.com/paper/2110.04236/) (2021)
- [Quantum Self-Attention Neural Networks for Text Classification](https://mlsanity.com/paper/2205.05625/) (2022)
- [When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing](https://mlsanity.com/paper/2203.03550/) (2022)
- [Avoiding Barren Plateaus with Classical Deep Neural Networks](https://mlsanity.com/paper/2205.13418/) (2022)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Compositionality as we see it, everywhere around us](https://mlsanity.com/paper/2110.05327/) (2021)
- [A Quantum Natural Language Processing Approach to Musical Intelligence](https://mlsanity.com/paper/2111.06741/) (2021)
- [On the Quantum-like Contextuality of Ambiguous Phrases](https://mlsanity.com/paper/2107.14589/) (2021)
- [Quantum Mathematics in Artificial Intelligence](https://mlsanity.com/paper/2101.04255/) (2021)
- [Diagrammatic Differentiation for Quantum Machine Learning](https://mlsanity.com/paper/2103.07960/) (2021)
- [How to make qubits speak](https://mlsanity.com/paper/2107.06776/) (2021)
- [Kindergarden quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus)](https://mlsanity.com/paper/2102.10984/) (2021)
- [Toward Quantum Machine Translation of Syntactically Distinct Languages](https://mlsanity.com/paper/2307.16576/) (2023)
- [Quantum Algorithms for Compositional Text Processing](https://mlsanity.com/paper/2408.06061/) (2024)
- [Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processing](https://mlsanity.com/paper/2102.02204/) (2021)
- [Grover's Algorithm for Question Answering](https://mlsanity.com/paper/2106.05299/) (2021)
- [A Quantum Natural Language Processing Approach to Pronoun Resolution](https://mlsanity.com/paper/2208.05393/) (2022)
- [Adapting Pre-trained Language Models for Quantum Natural Language Processing](https://mlsanity.com/paper/2302.13812/) (2023)
- [Hybrid Quantum-Classical Machine Learning for Sentiment Analysis](https://mlsanity.com/paper/2310.10672/) (2023)
- [Enabling Quantum Natural Language Processing for Hindi Language](https://mlsanity.com/paper/2312.01221/) (2023)
- [Functorial Language Models](https://mlsanity.com/paper/2103.14411/) (2021)
- [Lambek pregroups are Frobenius spiders in preorders](https://mlsanity.com/paper/2105.03038/) (2021)
- [A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models](https://mlsanity.com/paper/2306.03608/) (2023)
- [Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2405.16368/) (2024)
- [No-Free-Lunch Theories for Tensor-Network Machine Learning Models](https://mlsanity.com/paper/2412.05674/) (2024)
- [Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware](https://mlsanity.com/paper/2512.12710/) (2025)
- [Unclonability and Quantum Cryptanalysis: From Foundations to Applications](https://mlsanity.com/paper/2210.17545/) (2022)
- [Variational Quantum Classifiers for Natural-Language Text](https://mlsanity.com/paper/2303.02469/) (2023)
- [Quantum-machine-assisted Drug Discovery](https://mlsanity.com/paper/2408.13479/) (2024)

## Abstract (arXiv)

In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP. The language-modelling framework we employ is that of compositional distributional semantics (DisCoCat), which extends and complements the compositional structure of pregroup grammars. Within this model, the grammatical reduction of a sentence is interpreted as a diagram, encoding a specific interaction of words according to the grammar. It is this interaction which, together with a specific choice of word embedding, realises the meaning (or "semantics") of a sentence. Building on the formal quantum-like nature of such interactions, we present a method for mapping DisCoCat diagrams to quantum circuits. Our methodology is compatible both with NISQ devices and with established Quantum Machine Learning techniques, paving the way to near-term applications of quantum technology to natural language processing.