# Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications

arXiv: https://arxiv.org/abs/2504.09909 · HTML: https://mlsanity.com/paper/2504.09909/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 133 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 28 of 28)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (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)
- [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)
- [Quantum Algorithms for Compositional Natural Language Processing](https://mlsanity.com/paper/1608.01406/) (2016)
- [The Mathematics of Text Structure](https://mlsanity.com/paper/1904.03478/) (2019)
- [Quantum Language Model with Entanglement Embedding for Question Answering](https://mlsanity.com/paper/2008.09943/) (2020)
- [Grammar-aware sentence classification on quantum computers](https://mlsanity.com/paper/2012.03756/) (2020)
- [Concentration of Data Encoding in Parameterized Quantum Circuits](https://mlsanity.com/paper/2206.08273/) (2022)
- [CNM: An Interpretable Complex-valued Network for Matching](https://mlsanity.com/paper/1904.05298/) (2019)
- [Quantum-inspired Multimodal Fusion for Video Sentiment Analysis](https://mlsanity.com/paper/2103.10572/) (2021)
- [Quantum Natural Language Generation on Near-Term Devices](https://mlsanity.com/paper/2211.00727/) (2022)
- [The role of entanglement for enhancing the efficiency of quantum kernels towards classification](https://mlsanity.com/paper/2209.05142/) (2022)
- [A Quantum Many-body Wave Function Inspired Language Modeling Approach](https://mlsanity.com/paper/1808.09891/) (2018)
- [A hybrid classical-quantum workflow for natural language processing](https://mlsanity.com/paper/2004.06800/) (2020)
- [Language-independence of DisCoCirc's Text Circuits: English and Urdu](https://mlsanity.com/paper/2208.10281/) (2022)
- [MorphTE: Injecting Morphology in Tensorized Embeddings](https://mlsanity.com/paper/2210.15379/) (2022)
- [Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis](https://mlsanity.com/paper/2101.04406/) (2021)
- [Toward Quantum Machine Translation of Syntactically Distinct Languages](https://mlsanity.com/paper/2307.16576/) (2023)
- [Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit](https://mlsanity.com/paper/2305.19383/) (2023)
- [Quantum Algorithms for Compositional Text Processing](https://mlsanity.com/paper/2408.06061/) (2024)
- [Hybrid Quantum-Classical Machine Learning for Sentiment Analysis](https://mlsanity.com/paper/2310.10672/) (2023)
- [Looking at Vector Space and Language Models for IR using Density Matrices](https://mlsanity.com/paper/1401.1732/) (2014)
- [Encoding High-level Quantum Programs as SZX-diagrams](https://mlsanity.com/paper/2206.09376/) (2022)
- [Optimal Universal Quantum Encoding for Statistical Inference](https://mlsanity.com/paper/2404.08172/) (2024)

## Built on by (most cited first, 4 of 4)

- [Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware](https://mlsanity.com/paper/2512.12710/) (2025)
- [Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces](https://mlsanity.com/paper/2606.06941/) (2026)
- [Quantum Compositional NLP for Arabic: Grammar, Morphology, and Word Sense in Circuit Topology](https://mlsanity.com/paper/2607.14100/) (2026)
- [Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement](https://mlsanity.com/paper/2609.14529/) (2026)

## Abstract (arXiv)

In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.