Generalized Ansatz for High-Accuracy Quantum Sentiment Classification using Variational Quantum Algorithm Scheme

This paper investigates sentiment classification using QNLP within the Variational Quantum Algorithms (VQA) framework, which offers a practical approach for current hardware limitations by optimizing parameterized quantum circuits through classical computation. Designing an effective VQA ansatz is a key challenge because it directly influences the model’s expressiveness and trainability. To address this, a new ansatz called the GeneralQC ansatz is proposed by combining the preliminary SimpleSA ansatz with the circuit-centric quantum classifier, aiming to enhance performance by integrating the strengths of both methods. Three instantiations of the GeneralQC ansatz—H-CRx, Rx-CNOT, and Rx-CRz—are explored under the SimpleSA strategy of defining noun word boxes as fixed identity gates and are compared with both the baseline SimpleSA ansatz and versions with noun parameterization. The findings reveal that the Rx-CNOT and Rx-CRz models with non-parameterized nouns significantly outperform the SimpleSA ansatz, achieving an accuracy of 96.91%.