Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

Research on Distributional Compositional Categorical Model in Both Classical and Quantum Natural Language Processing

Tingyu Liu, Yingying Wei, Jiangtao Wang

Published Jul 5, 2024• 1 citation
Publisher

Abstract

Language models based on neural network primarily focus on learning patterns and representations from the input data, without explicitly modeling the grammatical structure of the text, thus often lacks of the consideration of syntax information. The distributional compositional categorical (DisCoCat) model is a framework that can both capture the distributional information of words and the syntactic structure of sentence. Thus, we research the application of DisCoCat models in both quantum and classical natural language processing. Specifically, in classical and quantum natural language processing, we respectively converted sentences into tensor networks and quantum circuits based on the DisCoCat model to get representation containing both semantic and syntactic information of language in classical and quantum computer. We conduct experiment for a text binary classification task in both classical and quantum natural language processing setting to verify the effectiveness of DisCoCat model. The experimental results shown that the combination of DisCoCat with quantum computing has significant potential.

DOI
10.1109/snpd61259.2024.10673943

Related papers

Quantum methods for managing ambiguity in natural language processing

Jurek Eisinger, Ward Gauderis, Lin de Huybrecht +1 more

The Categorical Compositional Distributional (DisCoCat) framework models meaning in natural language using the mathematical framework of quantum theory, expressed as formal diagrams. DisCoCat diagrams can be associated with tensor networks and quantum circuits. DisCoCat diagrams have been connected to density matrices in various contexts in Quantum Natural Language Processing (QNLP). Previous use of density matrices in QNLP entails modelling ambiguous words as probability distributions over basic words (e.g., the word queen might mean the reigning queen or the chess piece). In this article, we investigate the use of probability distributions over processes to account for syntactic ambiguity in sentences. The meanings of these sentences are represented by density matrices. We show how to create probability distributions on quantum circuits that represent the meanings of sentences and explain how this approach generalises tasks from the literature. We report on a proof-of-concept experiment to demonstrate the proposed theory.

arXiv:2504.00040•Quantum Machine Intelligence•Aug 3, 2026•0 citations

Quantum Natural Language Processing on Near-Term Quantum Computers

Konstantinos Meichanetzidis, Stefano Gogioso, Giovanni de Felice +3 more

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.

arXiv:2005.04147•Electronic Proceedings in Theoretical Computer Science•Sep 6, 2021•88 citations

A Survey of Quantum Natural Language Processing: From Compositional Models to NISQ-Era Empiricism

Arpan Phukan, Asif Ekbal

Quantum Natural Language Processing (QNLP) has emerged as a novel paradigm that leverages the principles of quantum mechanics to address fundamental challenges in language modeling, particularly in capturing compositional meaning.This survey charts the evolution of QNLP, from its theoretical foundations in the Distributional Compositional Categorical (DisCoCat) framework to its modern implementation on Noisy Intermediate-Scale Quantum (NISQ) hardware.We review the primary architectural approaches, including variational quantum circuits and tensor networks, and summarize the growing body of empirical work in tasks such as text classification, sentence similarity, and question answering.A recurring finding is the potential for QNLP models to achieve competitive performance with significantly fewer parameters than their classical counterparts.However, the field is critically constrained by the limitations of NISQ-era hardware.We conclude by discussing these challenges and outlining the future trajectory towards achieving a demonstrable quantum advantage and building more interpretable, efficient language models.

•Jan 1, 2025•0 citations

A gentle introduction to Quantum Natural Language Processing

Shervin Le Du, Senaida Hernández Santana, Scarpa, Giannicola

The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.

arXiv:2202.11766•Feb 23, 2022•4 citations

Multimodal Quantum Natural Language Processing: A Novel Framework for using Quantum Methods to Analyse Real Data

Hala Hawashin

Despite significant advances in quantum computing across various domains, research on applying quantum approaches to language compositionality - such as modeling linguistic structures and interactions - remains limited. This gap extends to the integration of quantum language data with real-world data from sources like images, video, and audio. This thesis explores how quantum computational methods can enhance the compositional modeling of language through multimodal data integration. Specifically, it advances Multimodal Quantum Natural Language Processing (MQNLP) by applying the Lambeq toolkit to conduct a comparative analysis of four compositional models and evaluate their influence on image-text classification tasks. Results indicate that syntax-based models, particularly DisCoCat and TreeReader, excel in effectively capturing grammatical structures, while bag-of-words and sequential models struggle due to limited syntactic awareness. These findings underscore the potential of quantum methods to enhance language modeling and drive breakthroughs as quantum technology evolves.

arXiv:2411.05023•Oct 29, 2024•0 citations

Adapting the DisCoCat-Model for Question Answering in the Chinese Language

Maximilian Balthasar Mansky, Franziska Wörle, Jonas Stein +2 more

We introduce quantum natural language processing for the Chinese language. Our approach focuses on Question-answering on a set of sentences, whether a particular sentence is truthful with respect to the whole corpus of sentences. We employ the Categorical Distributional Compositional (DisCoCat) model to translate sentences to valid quantum circuits. We achieve a fitting score of 97% on the test set. Our sentence set is also significantly larger than previous experiments. The results show general applicability of the framework to other languages. We also show that it can be used to introspect natural language models and provide new approaches to model explainability.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•1 citation
Language models based on neural network primarily focus on learning patterns and representations from the input data, without explicitly modeling the grammatical structure of the text, thus often lacks of the consideration of syntax information. The distributional compositional categorical (DisCoCat) model is a framework that can both capture the distributional information of words and the syntactic structure of sentence. Thus, we research the application of DisCoCat models in both quantum and classical natural language processing. Specifically, in classical and quantum natural language processing, we respectively converted sentences into tensor networks and quantum circuits based on the DisCoCat model to get representation containing both semantic and syntactic information of language in classical and quantum computer. We conduct experiment for a text binary classification task in both classical and quantum natural language processing setting to verify the effectiveness of DisCoCat model. The experimental results shown that the combination of DisCoCat with quantum computing has significant potential.
The Categorical Compositional Distributional (DisCoCat) framework models meaning in natural language using the mathematical framework of quantum theory, expressed as formal diagrams. DisCoCat diagrams can be associated with tensor networks and quantum circuits. DisCoCat diagrams have been connected to density matrices in various contexts in Quantum Natural Language Processing (QNLP). Previous use of density matrices in QNLP entails modelling ambiguous words as probability distributions over basic words (e.g., the word queen might mean the reigning queen or the chess piece). In this article, we investigate the use of probability distributions over processes to account for syntactic ambiguity in sentences. The meanings of these sentences are represented by density matrices. We show how to create probability distributions on quantum circuits that represent the meanings of sentences and explain how this approach generalises tasks from the literature. We report on a proof-of-concept experiment to demonstrate the proposed theory.
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.
Quantum Natural Language Processing (QNLP) has emerged as a novel paradigm that leverages the principles of quantum mechanics to address fundamental challenges in language modeling, particularly in capturing compositional meaning.This survey charts the evolution of QNLP, from its theoretical foundations in the Distributional Compositional Categorical (DisCoCat) framework to its modern implementation on Noisy Intermediate-Scale Quantum (NISQ) hardware.We review the primary architectural approaches, including variational quantum circuits and tensor networks, and summarize the growing body of empirical work in tasks such as text classification, sentence similarity, and question answering.A recurring finding is the potential for QNLP models to achieve competitive performance with significantly fewer parameters than their classical counterparts.However, the field is critically constrained by the limitations of NISQ-era hardware.We conclude by discussing these challenges and outlining the future trajectory towards achieving a demonstrable quantum advantage and building more interpretable, efficient language models.
The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.
Despite significant advances in quantum computing across various domains, research on applying quantum approaches to language compositionality - such as modeling linguistic structures and interactions - remains limited. This gap extends to the integration of quantum language data with real-world data from sources like images, video, and audio. This thesis explores how quantum computational methods can enhance the compositional modeling of language through multimodal data integration. Specifically, it advances Multimodal Quantum Natural Language Processing (MQNLP) by applying the Lambeq toolkit to conduct a comparative analysis of four compositional models and evaluate their influence on image-text classification tasks. Results indicate that syntax-based models, particularly DisCoCat and TreeReader, excel in effectively capturing grammatical structures, while bag-of-words and sequential models struggle due to limited syntactic awareness. These findings underscore the potential of quantum methods to enhance language modeling and drive breakthroughs as quantum technology evolves.
We introduce quantum natural language processing for the Chinese language. Our approach focuses on Question-answering on a set of sentences, whether a particular sentence is truthful with respect to the whole corpus of sentences. We employ the Categorical Distributional Compositional (DisCoCat) model to translate sentences to valid quantum circuits. We achieve a fitting score of 97% on the test set. Our sentence set is also significantly larger than previous experiments. The results show general applicability of the framework to other languages. We also show that it can be used to introspect natural language models and provide new approaches to model explainability.