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

Quantum-Informed Recursive Optimization for Literary Semantics and Natural Language Generation: A Compositional Analysis of Dante's Divine Comedy

Javier Julián Enríquez

Published Aug 26, 2026• 0 citations
Publisher

Abstract

This study explores Quantum Natural Language Processing (QNLP) by examining how quantum computation may enhance Natural Language Generation (NLG). It proposes a theoretical framework for transforming complex literary texts, with Dante Alighieri’s Divine Comedy as the central example, into recursive algebraic structures suitable for quantum-informed processing. Drawing on category theory, tensor algebra, compositional distributional semantics, and hybrid quantum-classical optimization, the framework models grammatical and semantic relations in ways that may improve long-range coherence, ambiguity resolution, and context-sensitive text generation. The principal objective is to formulate a rigorous pathway from literary analysis to quantum-compatible semantic representation and recursive optimization for NLG.

DOI
10.13140/rg.2.2.31785.35680

Related papers

Quantum Calculations to Transforming Dante into Recursive Algebra for Enhanced NLG

Javier Julián Enríquez

This research explores the frontier of Quantum Natural Language Processing (QNLP) by investigating the application of quantum computation to enhancing Natural Language Generation (NLG). Specifically, it proposes a novel approach to transforming complex literary texts, exemplified by Dante Alighieri's Divine Comedy, into recursive algebraic structures suitable for quantum processing. By leveraging category theory and tensor algebra, this methodology aims to model grammatical and semantic structures, enabling quantum computers to better handle context and ambiguity than classical methods. The core objective is to develop a framework for quantum-informed recursive optimization algorithms that can be applied to NLG tasks, thereby improving text generation capabilities.

•Jun 18, 2026•0 citations

Semantic Analysis of Auto-Generated Sentences Using Quantum Natural Language Processing

J. Kavya, K. Krishnaveni, K. Namratha +2 more

Abstract: Quantum Natural Language Processing (QNLP) represents a pioneering approach to understanding and analyzing natural language by leveraging the principles of quantum computing. This project aims to explore the semantic analysis of autogenerated sentences using QNLP techniques. Traditional Natural Language Processing (NLP) methods have achieved significant milestones in language understanding and generation; however, they often struggle with the intricacies of context, ambiguity, and the vast computational resources required for complex tasks. QNLP offers a novel paradigm by utilizing the superposition and entanglement properties of quantum states, potentially revolutionizing how semantic meaning is extracted and processed. The primary objective of this project is to develop a QNLP framework capable of performing semantic analysis on autogenerated sentences. We will investigate the application of quantum algorithms and quantum machine learning models to decode the semantic structure and context of sentences generated by language models. This involves encoding textual data into quantum states, applying quantum gates and circuits to manipulate these states, and measuring the outcomes to interpret semantic information. By integrating quantum computing with NLP, this project seeks to achieve higher accuracy and efficiency in understanding complex language patterns. The anticipated outcomes include a comprehensive evaluation of QNLP's performance in semantic analysis, a comparison with classical NLP methods, and potential advancements in language processing tasks such as sentiment analysis, language translation, and text summarization. This project will contribute to the growing body of research at the intersection of quantum computing and artificial intelligence, paving the way for innovative solutions in the field of natural language processing.

International Journal for Research in Applied Science and Engineering Technology•Nov 24, 2024•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

Core Claim: A quantum geometry-based natural language processing optimization method proposes to leverage the unique properties of quantum geometry to enhance the performance of natural language processing models, specifically in text generation and semantic understanding.

Jincheng Zhang

This paper presents a novel quantum geometry-based natural language processing optimization approach. The core idea is to utilize quantum geometry, a mathematical framework with geometric properties, to optimize the parameters and structure of natural language processing models. We hypothesize that quantum geometry can enhance model performance by introducing geometric constraints that promote better representations and generalization. The paper details the methodology, including the formulation of a quantum geometric optimization algorithm and a preliminary analysis of its potential impact on key NLP tasks. The research explores how incorporating quantum geometric principles can lead to improved text generation quality and deeper semantic understanding. This work contributes to the field of natural language processing by investigating a new approach to model optimization that leverages the potential of quantum geometry. The paper concludes with a discussion of the limitations of the proposed method and outlines potential avenues for future research.

•Sep 4, 2026•0 citations

Category Theory for Quantum Natural Language Processing

Alexis Toumi

This thesis introduces quantum natural language processing (QNLP) models based on a simple yet powerful analogy between computational linguistics and quantum mechanics: grammar as entanglement. The grammatical structure of text and sentences connects the meaning of words in the same way that entanglement structure connects the states of quantum systems. Category theory allows to make this language-to-qubit analogy formal: it is a monoidal functor from grammar to vector spaces. We turn this abstract analogy into a concrete algorithm that translates the grammatical structure onto the architecture of parameterised quantum circuits. We then use a hybrid classical-quantum algorithm to train the model so that evaluating the circuits computes the meaning of sentences in data-driven tasks. The implementation of QNLP models motivated the development of DisCoPy (Distributional Compositional Python), the toolkit for applied category theory of which the first chapter gives a comprehensive overview. String diagrams are the core data structure of DisCoPy, they allow to reason about computation at a high level of abstraction. We show how they can encode both grammatical structures and quantum circuits, but also logical formulae, neural networks or arbitrary Python code. Monoidal functors allow to translate these abstract diagrams into concrete computation, interfacing with optimised task-specific libraries. The second chapter uses DisCopy to implement QNLP models as parameterised functors from grammar to quantum circuits. It gives a first proof-of-concept for the more general concept of functorial learning: generalising machine learning from functions to functors by learning from diagram-like data. In order to learn optimal functor parameters via gradient descent, we introduce the notion of diagrammatic differentiation: a graphical calculus for computing the gradients of parameterised diagrams.

arXiv:2212.06615•Dec 13, 2022•4 citations

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

Tingyu Liu, Yingying Wei, Jiangtao Wang

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.

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)•Jul 5, 2024•1 citation
This study explores Quantum Natural Language Processing (QNLP) by examining how quantum computation may enhance Natural Language Generation (NLG). It proposes a theoretical framework for transforming complex literary texts, with Dante Alighieri’s Divine Comedy as the central example, into recursive algebraic structures suitable for quantum-informed processing. Drawing on category theory, tensor algebra, compositional distributional semantics, and hybrid quantum-classical optimization, the framework models grammatical and semantic relations in ways that may improve long-range coherence, ambiguity resolution, and context-sensitive text generation. The principal objective is to formulate a rigorous pathway from literary analysis to quantum-compatible semantic representation and recursive optimization for NLG.
This research explores the frontier of Quantum Natural Language Processing (QNLP) by investigating the application of quantum computation to enhancing Natural Language Generation (NLG). Specifically, it proposes a novel approach to transforming complex literary texts, exemplified by Dante Alighieri's Divine Comedy, into recursive algebraic structures suitable for quantum processing. By leveraging category theory and tensor algebra, this methodology aims to model grammatical and semantic structures, enabling quantum computers to better handle context and ambiguity than classical methods. The core objective is to develop a framework for quantum-informed recursive optimization algorithms that can be applied to NLG tasks, thereby improving text generation capabilities.
Abstract: Quantum Natural Language Processing (QNLP) represents a pioneering approach to understanding and analyzing natural language by leveraging the principles of quantum computing. This project aims to explore the semantic analysis of autogenerated sentences using QNLP techniques. Traditional Natural Language Processing (NLP) methods have achieved significant milestones in language understanding and generation; however, they often struggle with the intricacies of context, ambiguity, and the vast computational resources required for complex tasks. QNLP offers a novel paradigm by utilizing the superposition and entanglement properties of quantum states, potentially revolutionizing how semantic meaning is extracted and processed. The primary objective of this project is to develop a QNLP framework capable of performing semantic analysis on autogenerated sentences. We will investigate the application of quantum algorithms and quantum machine learning models to decode the semantic structure and context of sentences generated by language models. This involves encoding textual data into quantum states, applying quantum gates and circuits to manipulate these states, and measuring the outcomes to interpret semantic information. By integrating quantum computing with NLP, this project seeks to achieve higher accuracy and efficiency in understanding complex language patterns. The anticipated outcomes include a comprehensive evaluation of QNLP's performance in semantic analysis, a comparison with classical NLP methods, and potential advancements in language processing tasks such as sentiment analysis, language translation, and text summarization. This project will contribute to the growing body of research at the intersection of quantum computing and artificial intelligence, paving the way for innovative solutions in the field of natural language processing.
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.
This paper presents a novel quantum geometry-based natural language processing optimization approach. The core idea is to utilize quantum geometry, a mathematical framework with geometric properties, to optimize the parameters and structure of natural language processing models. We hypothesize that quantum geometry can enhance model performance by introducing geometric constraints that promote better representations and generalization. The paper details the methodology, including the formulation of a quantum geometric optimization algorithm and a preliminary analysis of its potential impact on key NLP tasks. The research explores how incorporating quantum geometric principles can lead to improved text generation quality and deeper semantic understanding. This work contributes to the field of natural language processing by investigating a new approach to model optimization that leverages the potential of quantum geometry. The paper concludes with a discussion of the limitations of the proposed method and outlines potential avenues for future research.
This thesis introduces quantum natural language processing (QNLP) models based on a simple yet powerful analogy between computational linguistics and quantum mechanics: grammar as entanglement. The grammatical structure of text and sentences connects the meaning of words in the same way that entanglement structure connects the states of quantum systems. Category theory allows to make this language-to-qubit analogy formal: it is a monoidal functor from grammar to vector spaces. We turn this abstract analogy into a concrete algorithm that translates the grammatical structure onto the architecture of parameterised quantum circuits. We then use a hybrid classical-quantum algorithm to train the model so that evaluating the circuits computes the meaning of sentences in data-driven tasks. The implementation of QNLP models motivated the development of DisCoPy (Distributional Compositional Python), the toolkit for applied category theory of which the first chapter gives a comprehensive overview. String diagrams are the core data structure of DisCoPy, they allow to reason about computation at a high level of abstraction. We show how they can encode both grammatical structures and quantum circuits, but also logical formulae, neural networks or arbitrary Python code. Monoidal functors allow to translate these abstract diagrams into concrete computation, interfacing with optimised task-specific libraries. The second chapter uses DisCopy to implement QNLP models as parameterised functors from grammar to quantum circuits. It gives a first proof-of-concept for the more general concept of functorial learning: generalising machine learning from functions to functors by learning from diagram-like data. In order to learn optimal functor parameters via gradient descent, we introduce the notion of diagrammatic differentiation: a graphical calculus for computing the gradients of parameterised diagrams.
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.