# A hybrid classical-quantum workflow for natural language processing

arXiv: https://arxiv.org/abs/2004.06800 · HTML: https://mlsanity.com/paper/2004.06800/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [How to make qubits speak](https://mlsanity.com/paper/2107.06776/) (2021)
- [Toward Quantum Machine Translation of Syntactically Distinct Languages](https://mlsanity.com/paper/2307.16576/) (2023)
- [Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications](https://mlsanity.com/paper/2504.09909/) (2025)
- [Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processing](https://mlsanity.com/paper/2102.02204/) (2021)
- [A gentle introduction to Quantum Natural Language Processing](https://mlsanity.com/paper/2202.11766/) (2022)
- [Design and Implementation of a Quantum Kernel for Natural Language Processing](https://mlsanity.com/paper/2205.06409/) (2022)

## Abstract (arXiv)

Natural language processing (NLP) problems are ubiquitous in classical computing, where they often require significant computational resources to infer sentence meanings. With the appearance of quantum computing hardware and simulators, it is worth developing methods to examine such problems on these platforms. In this manuscript we demonstrate the use of quantum computing models to perform NLP tasks, where we represent corpus meanings, and perform comparisons between sentences of a given structure. We develop a hybrid workflow for representing small and large scale corpus data sets to be encoded, processed, and decoded using a quantum circuit model. In addition, we provide our results showing the efficacy of the method, and release our developed toolkit as an open software suite.