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.
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.
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.
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.
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.
Quantum Natural Language Processing (QNLP) is built on a novel, category theoretical quantum machine learning model, which we use to solve a well-researched problem in the database field: estimating metrics for SQL queries. The metrics we focus on are latency (i.e., the execution time), cost (i.e., a value of how expensive the query is in total), and cardinality (i.e., the number of rows in the result table). The critical link between QNLP and the database problem lies in using grammars, such as context-free grammar. Both natural language and SQL queries admit grammatical presentations, which allow us to develop a comprehensive framework for applying QNLP methods to solve the query estimation problem. The model comprises an encoding mechanism and a training phase, integrating classical and quantum subroutines. The encoding mechanism transforms SQL queries into parametrized quantum circuits as in QNLP. Classical optimization algorithms optimize the circuit parameters during training to predict query metrics. Comparing our model to the QNLP models, our model achieves accuracy comparable to the QNLP model in binary classification tasks. Additionally, we expand upon previous research by implementing multi-class classification tasks and comparing the cardinality estimation results to those of state-of-the-art databases. Our theoretical analysis of the quantum machine learning model includes calculations of its expressibility and entangling capabilities. The analysis reveals that the model possesses beneficial properties, making it suitable to be executed on near-term quantum hardware.
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.