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.
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.
Recent advances in quantum computing have led to progress in exploring quantum applications across diverse fields, including databases and data management. This work presents a quantum machine learning model that tackles the challenge of estimating metrics, such as cardinalities, execution times, and costs, for SQL queries in relational databases. Precise estimations are crucial for the query optimizer to optimize query processing in relational databases efficiently. Our proposed quantum machine learning model consists of a novel query encoding mechanism, which maps SQL queries into high-dimensional Hilbert spaces using grammatical representations of the queries. The encoding mechanism translates SQL queries into parameterized quantum circuits, forming the core of the quantum machine learning model. The parameters in this model are tuned using standard quantum machine learning techniques. This encoding was first developed in quantum natural language processing (QNLP), and this work demonstrates its natural application in database optimization. Because the encoding mechanism is mathematically robust, the quantum machine learning model is also explainable, allowing us to draw a one-to-one correspondence between the elements in SQL queries and the model's parameters. The method is also scalable because it consists of multiple circuits, and we train and evaluate the model with hundreds of queries. Compared to previous research, our model achieves high accuracy, supporting the results obtained in the original QNLP research. We extend the previous QNLP work by adding 4-class and 8 -class classification tasks and comparing the cardinality estimation results with those from state-of-the-art databases. This model shows a competitive accuracy compared to the estimators in the databases. We theoretically analyze the quantum machine learning model by calculating its expressibility and entangling capabilities, which have been suggested as representative metrics describing the model's trainability and learnability features.
[EN] Quantum computing is widely regarded as one of the scientific fields with the greatest transformative potential. Its applications are already being explored in areas such as cybersecurity, chemistry, and machine learning. Within the domain of quantum machine learning, one emerging subfield is quantum natural language processing (QNLP), which seeks to leverage quantum algorithms to address complex language-related tasks more efficiently than classical counterparts. This thesis begins with a systematic literature review of the current landscape of quantum machine learning, focusing particularly on the design, development, and deployment of QNLP models on real quantum hardware. The SLR identifies 94 relevant articles that utilize quantum machine learning techniques and algorithms, highlighting their implementation via computational quantum circuits, or ansatzes. The main types of algorithms found include quantum adaptations of classical models such as support vector machines and k-nearest neighbors, as well as quantum neural networks, one of the most prominent applications being image classification. In addition, this work offers both quantitative and qualitative analyses, integrating a comprehensive set of experiments conducted on quantum simulators and real quantum devices. A significant contribution of this work lies in the design and evaluation of these new quantum circuit architectures, tailored to optimize expressibility, entanglement, and trainability. Empirical analysis shows that model accuracy tends to exhibit an inverse relationship with ansatz expressibility and a direct correlation with entanglement capability. In parallel, it contributes to the field by proposing novel ansatz architectures and applying circuit knitting techniques to overcome current hardware limitations. Ultimately, this thesis demonstrates the feasibility of hybrid quantum-classical approaches in QNLP and highlights their potential to be extended to support more complex and scalable real-world language applications.
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.
We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP), and do so in quantum computer scientist friendly terms. We opted for an expository presentation style, and provide references for supporting empirical evidence and formal statements concerning mathematical generality. We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar. In particular, the fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar. Quantum speed-up for QNLP tasks has already been established in previous work with Will Zeng. Here we provide a broader range of tasks which all enjoy the same advantage. Diagrammatic reasoning is at the heart of QNLP. Firstly, the quantum model interprets language as quantum processes via the diagrammatic formalism of categorical quantum mechanics. Secondly, these diagrams are via ZX-calculus translated into quantum circuits. Parameterisations of meanings then become the circuit variables to be learned. Our encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.
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) refers to the many approaches developed within the discipline as a result of the convergence of natural language processing (NLP) and quantum computing in recent years. This multidisciplinary field involves applying quantum mechanical insights to fundamental aspects of language processing across a wide range of NLP activities. Existing methods span a broad spectrum from theoretical demonstrations of the quantum advantage to actual implementations of algorithms using quantum hardware. This work aims to do just that by bringing together, in one place, the many different methods that have been applied to the problem. Theoretical studies and practices that have been implemented on classical or quantum hardware are separated here. The categorization also takes into account the goal of each strategy. Syntax-semantic representation is an example of a general-purpose task. In this chapter, the benefits of QNLP in terms of efficiency and methodology are dissected. In addition, it explores how QNLP techniques might be utilized in place of stateof- the-art deep learning-based methodologies.