# Grammar-aware sentence classification on quantum computers

arXiv: https://arxiv.org/abs/2012.03756 · HTML: https://mlsanity.com/paper/2012.03756/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 17 of 17)

- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications](https://mlsanity.com/paper/2504.09909/) (2025)
- [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)
- [Compositional Concept Generalization with Variational Quantum Circuits](https://mlsanity.com/paper/2509.09541/) (2025)
- [Peptide Binding Classification on Quantum Computers](https://mlsanity.com/paper/2311.15696/) (2023)
- [Enabling Quantum Natural Language Processing for Hindi Language](https://mlsanity.com/paper/2312.01221/) (2023)
- [Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach](https://mlsanity.com/paper/2312.00688/) (2023)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Quantum Models with Multi-Stage Training for Compositional Concept Generalization](https://mlsanity.com/paper/2608.15601/) (2026)
- [Unclonability and Quantum Cryptanalysis: From Foundations to Applications](https://mlsanity.com/paper/2210.17545/) (2022)
- [Traitement quantique des langues : 'etat de l'art](https://mlsanity.com/paper/2406.15370/) (2024)
- [Multimodal Structure-Aware Quantum Data Processing](https://mlsanity.com/paper/2411.04242/) (2024)
- [Efficient Generation of Parameterised Quantum Circuits from Large Texts](https://mlsanity.com/paper/2505.13208/) (2025)
- [CLAQS: Compact Learnable All-Quantum Token Mixer with Shared-ansatz for Text Classification](https://mlsanity.com/paper/2510.06532/) (2025)
- [QOuLiPo: What a quantum computer sees when it reads a book](https://mlsanity.com/paper/2605.14188/) (2026)
- [Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation](https://mlsanity.com/paper/2610.12806/) (2026)

## Abstract (arXiv)

Natural language processing (NLP) is at the forefront of great advances in contemporary AI, and it is arguably one of the most challenging areas of the field. At the same time, in the area of Quantum Computing (QC), with the steady growth of quantum hardware and notable improvements towards implementations of quantum algorithms, we are approaching an era when quantum computers perform tasks that cannot be done on classical computers with a reasonable amount of resources. This provides a new range of opportunities for AI, and for NLP specifically. In this work, we work with the Categorical Distributional Compositional (DisCoCat) model of natural language meaning, whose underlying mathematical underpinnings make it amenable to quantum instantiations. Earlier work on fault-tolerant quantum algorithms has already demonstrated potential quantum advantage for NLP, notably employing DisCoCat. In this work, we focus on the capabilities of noisy intermediate-scale quantum (NISQ) hardware and perform the first implementation of an NLP task on a NISQ processor, using the DisCoCat framework. Sentences are instantiated as parameterised quantum circuits; word-meanings are embedded in quantum states using parameterised quantum-circuits and the sentence's grammatical structure faithfully manifests as a pattern of entangling operations which compose the word-circuits into a sentence-circuit. The circuits' parameters are trained using a classical optimiser in a supervised NLP task of binary classification. Our novel QNLP model shows concrete promise for scalability as the quality of the quantum hardware improves in the near future and solidifies a novel branch of experimental research at the intersection of QC and AI.