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International Conference on Computing, Sciences and Communications (ICCSC)

Experimentation Analysis of VQC and QSVM on Sentence Classification in Quantum Paradigm

Jayaprakash Sunkavalli, B. Madhav Rao, Miriyala Trinath Basu, Harish Dutt Sharma, P. M. Ashok Kumar, Ketan Anand

Published Oct 24, 2024• 2 citations
Publisher

Abstract

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.

DOI
10.1109/iccsc62048.2024.10830384

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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.
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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.
Emotion Recognition plays an important role in recognizing human behavior. Human behavior may be represented in text, speech, and facial expressions. In traditional form, natural language processing known as NLP is used to extract features to evaluate emotions. With the evolution of Quantum Computing, Quantum Machine learning often called QML is one of the research areas used to evaluate emotions. This paper is designed to compare traditional ML models with QML models to analyze the sentiments and emotions of multimodal data. Here, NLP techniques are used for preprocessing. A hybrid model is built by combining NLP and QML. The preprocessed data are transmitted into quantum circuits for computation, and its results are compared with conventional machine learning models. This evaluation identifies QML as a promising future as it is useful for large and complex data sets with high dimensions. This study highlights the potential of QML in NLP and outlines the challenges such as encoding and hardware limitations.
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Sentiment analysis is a branch of Natural Language Processing (NLP) which goal is to assign sentiments or emotions to particular sentences or words. Performing this task is particularly useful for companies wishing to take into account customer feedback through chatbots or verbatim. This has been done extensively in the literature using various approaches, ranging from simple models to deep transformer neural networks. In this paper, we will tackle sentiment analysis in the Noisy Intermediate Scale Computing (NISQ) era, using the DisCoCat model of language. We will first present the basics of quantum computing and the DisCoCat model. This will enable us to define a general framework to perform NLP tasks on a quantum computer. We will then extend the two-class classification that was performed by Lorenz et al. (2021) to a four-class sentiment analysis experiment on a much larger dataset, showing the scalability of such a framework.