# Quantum Algorithms for Compositional Natural Language Processing

arXiv: https://arxiv.org/abs/1608.01406 · HTML: https://mlsanity.com/paper/1608.01406/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2016.

## Built on by (most cited first, 24 of 24)

- [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)
- [Applying QNLP to sentiment analysis in finance](https://mlsanity.com/paper/2307.11788/) (2023)
- [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)
- [Quantum Machine Learning for Finance](https://mlsanity.com/paper/2109.04298/) (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)
- [Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications](https://mlsanity.com/paper/2504.09909/) (2025)
- [Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit](https://mlsanity.com/paper/2305.19383/) (2023)
- [A Pipeline For Discourse Circuits From CCG](https://mlsanity.com/paper/2311.17892/) (2023)
- [Quantum Algorithms for Compositional Text Processing](https://mlsanity.com/paper/2408.06061/) (2024)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [A gentle introduction to Quantum Natural Language Processing](https://mlsanity.com/paper/2202.11766/) (2022)
- [Adapting Pre-trained Language Models for Quantum Natural Language Processing](https://mlsanity.com/paper/2302.13812/) (2023)
- [Quantum Transfer Learning for Acceptability Judgements](https://mlsanity.com/paper/2401.07777/) (2024)
- [Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2301.08167/) (2023)
- [Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing](https://mlsanity.com/paper/2405.11783/) (2024)
- [Fast Extraction of Word Embedding from Q-contexts](https://mlsanity.com/paper/2109.07084/) (2021)
- [Design and Implementation of a Quantum Kernel for Natural Language Processing](https://mlsanity.com/paper/2205.06409/) (2022)
- [Implications of Deep Circuits in Improving Quality of Quantum Question Answering](https://mlsanity.com/paper/2305.07374/) (2023)
- [Traitement quantique des langues : 'etat de l'art](https://mlsanity.com/paper/2406.15370/) (2024)
- [A Hybrid Classical-Quantum Fine Tuned BERT for Text Classification](https://mlsanity.com/paper/2511.17677/) (2025)

## Abstract (arXiv)

We propose a new application of quantum computing to the field of natural language processing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In (Coecke, Sadrzadeh and Clark, 2010), the authors introduce such a model (the CSC model) based on tensor product composition. While this algorithm has many advantages, its implementation is hampered by the large classical computational resources that it requires. In this work we show how computational shortcomings of the CSC approach could be resolved using quantum computation (possibly in addition to existing techniques for dimension reduction). We address the value of quantum RAM (Giovannetti,2008) for this model and extend an algorithm from Wiebe, Braun and Lloyd (2012) into a quantum algorithm to categorize sentences in CSC. Our new algorithm demonstrates a quadratic speedup over classical methods under certain conditions.