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.
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.
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.
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.
This research introduces a new paradigm for sentiment analysis on patient medication reviews through the application of quantum computing to improve machine learning models. A pre-existing dataset of medication reviews from the UCI Machine Learning repository was utilized, involving Quantum Natural Language Processing (QNLP) techniques, namely the application of quantum embeddings produced by quantum circuits to improve the performance of sentiment classification. Experimental findings indicate that improved models through quantum techniques, like the Quantum Random Forest, outperform conventional models in terms of accuracy and precision. These improvements are ascribed to quantum embeddings’ capacity to efficiently represent complex linguistic patterns in high-dimensional data. Additionally, the study uses robust validation techniques including performance benchmarking and cross-validation to guarantee reliability. Through a scalable and context-aware framework for natural language comprehension, this study highlights the revolutionary potential of QNLP in improving sentiment analysis in the healthcare industry.
Quantum computers has the capacity to enhance the speed of intricate calculations, achieve parallelism via superpositions, and effectively process vast amounts of data. In addition, the domain of natural language processing (NLP) is swiftly drawing the attention of academics and engineers that want to construct more extensive computational models for NLP. Hence, the use of quantum technology in NLP endeavours, particularly in sentiment categorization, has promising prospects for further advancement. In this research work, a detailed analysis of different sentiment classification methods are investigated that are interpreted with quantum computing models. To be precise the Quantum Long Short Term Memory (QLSTM) and Quantum Support Vector Machines (QSVM) are examined to determine the efficiency among the models. The results are simulated in Qiskit simulator and real time quantum computer Qiskit_ibm_osaka to evaluate the examined models. The results shows the significance of the experimentation and it is proven using Accuracy, precision and recall as performance metrices. On an average, in terms of accuracy the proposed model outperforms existing methods with 8.9%, On precision the proposed model outperforms 11.1% compared to existing methods. And with respect to recall the proposed model outperforms 6.7% compared to existing methods.
This research paper primarily investigates the application and performance of traditional machine learning and quantum computing in Natural Language Processing (NLP), with a focus on sentiment analysis tasks. By comparing the accuracy, efficiency, and scalability of these two technologies, the study aims to reveal the potential of quantum computing in handling complex NLP tasks and to provide data support for future technology choices and research directions. The paper also details the use of 1M DB and NLTK movie review datasets for experiments and discusses the experimental design, performance evaluation results, and technical challenges faced by both methods.
This research aims to improve the robustness of Quantum Natural Language Processing (QNLP) models in the presence of noise. Using Lambeq, an open-source library for quantum NLP, we focus on sentiment classification tasks to eval-uate the impact of noisy quantum models. Our study involves the application of three types of optimizers: Simultaneous Perturbation Stochastic Approximation (SPSA), Nelder-Mead, and Adam. The performance and resilience of these optimizers are assessed under various noise conditions to determine their effectiveness in maintaining model accuracy and stability. The research shows that the Adam optimizer outperforms SPSA and NelderMead in QNLP simple sentiment analysis tasks, both in noiseless and noisy environments. Adam achieved 96.67 % accuracy in noiseless conditions, significantly higher than SPSA's 83.33 % and Nelder-Mead's 53.33%, highlighting its superior performance in optimizing quantum circuits. Moreover, in noisy simulation, Adam outperforms others with 70.00% accuracy performance. The work is useful for NLP implementations in quantum computers. This research is essential because the noisy nature of current quan-tum computers makes it difficult for QNLP models to perform accurately and reliably.