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.
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.
The quantum computing made complex computations may be executed at light speed, superpositions allow for parallelism, and massive quantities of data can be efficiently processed. Furthermore, researchers and engineers are quickly becoming interested in natural language processing (NLP) as a topic for developing more comprehensive computational models of NLP. Therefore, there is hope for future progress in NLP efforts that use quantum technology, especially in emotion classification. In this study, we use quantum computing models to delve deeply into two sentiment categorization approaches. To be more specific, the effectiveness of the models is evaluated by looking at VQC and QSVM. The analyzed models are evaluated by simulations in the Qiskit simulator and the real-time quantum computer Qiskit_ibm_osaka. Results demonstrate the importance of the experiment and validate it using positive predicted value, sensitivity and Fl-Score as performance metrices.
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 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.
This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.
With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data.
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.