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

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.