Quantum physics and mechanics have demonstrated significant advances and promising results in different areas using the current near-term devices. One emerging subarea in quantum machine learning is quantum natural language processing, which combines quantum computing advantages and speedups with language processing algorithms to create and perform natural language tasks such as text classification or generation. The libraries and toolboxes used in this subarea include DisCoPy and lambeq, which are used to transform sentences into string diagrams or monoidal functors, convert these diagrams into quantum circuits or ansatz and embed it into a quantum model. In this study, we used both libraries with different text-based datasets to perform sentiment analysis via classification. To do so, we create synthetic datasets to train the different models. After we obtain satisfactory results, we test the resulting models with known datasets. Despite its promising results, quantum natural language processing is far from achieving its full potential. To achieve this potential, the quantum software and hardware must be improved to make them suitable for use with more extensive and complex datasets and other tasks.
Quantum physics and mechanics have demonstrated significant advances and promising results in different areas using the current near-term devices. One emerging subarea in quantum machine learning is quantum natural language processing, which combines quantum computing advantages and speedups with language processing algorithms to create and perform natural language tasks such as text classification or generation. The libraries and toolboxes used in this subarea include DisCoPy and lambeq, which are used to transform sentences into string diagrams or monoidal functors, convert these diagrams into quantum circuits or ansatz and embed it into a quantum model. In this study, we used both libraries with different text-based datasets to perform sentiment analysis via classification. To do so, we create synthetic datasets to train the different models. After we obtain satisfactory results, we test the resulting models with known datasets. Despite its promising results, quantum natural language processing is far from achieving its full potential. To achieve this potential, the quantum software and hardware must be improved to make them suitable for use with more extensive and complex datasets and other tasks.
Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.
The meeting between Natural Language Processing (NLP) and Quantum Computing has been very successful in recent years, leading to the development of several approaches of the so-called Quantum Natural Language Processing (QNLP). This is a hybrid field in which the potential of quantum mechanics is exploited and applied to critical aspects of language processing, involving different NLP tasks. Approaches developed so far span from those that demonstrate the quantum advantage only at the theoretical level to the ones implementing algorithms on quantum hardware. This paper aims to list the approaches developed so far, categorizing them by type, i.e., theoretical work and those implemented on classical or quantum hardware; by task, i.e., general purpose such as syntax-semantic representation or specific NLP tasks, like sentiment analysis or question answering; and by the resource used in the evaluation phase, i.e., whether a benchmark dataset or a custom one has been used. The advantages offered by QNLP are discussed, both in terms of performance and methodology, and some considerations about the possible usage QNLP approaches in the place of state-of-the-art deep learning-based ones are given.
The advent of quantum computing has led to increase in computational capabilities, leading to exponential speedups for problems that takes lot of time when solved using classical computing. Natural Language Processing (NLP), an application of artificial intelligence is concerned with enabling machines to understand, interpret, and generate human language. We may speed up the implementation of Natural Language Processing tasks by the integration of quantum algorithms. This paper shows the impact of quantum computing on NLP, explaining how quantum algorithms play an important role in redefining traditional language processing paradigms. This paper discusses various quantum algorithms applied to NLP, including quantum machine learning approaches for text classification, clustering, and sentiment analysis, as well as quantum inspired neural networks for advanced language modeling. This paper discusses novel quantum NLP models, such as quantum variational algorithms and quantum enhanced transformers, which promise significant improvements in speed, accuracy, and contextual understanding. This paper also addresses the limitations and challenges of quantum NLP., including the current state of quantum hardware, noise issues, and the need for robust quantum programming frameworks. It aims to provide a comprehensive overview of how quantum algorithms are revolutionizing NLP and what the future holds for this emerging interdisciplinary field
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) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a quantum computer. lambeq supports syntactic parsing, rewriting and simplification of string diagrams, ansatz creation and manipulation, as well as a number of compositional models for preparing quantum-friendly representations of sentences, employing various degrees of syntax sensitivity. We present the generic architecture and describe the most important modules in detail, demonstrating the usage with illustrative examples. Further, we test the toolkit in practice by using it to perform a number of experiments on simple NLP tasks, implementing both classical and quantum pipelines.