Quantum-inspired Neural Network Based on Stochastic Liouville-von Neumann Equation for Sentiment Classification

Kehuan Yan, Peichao Lai, Qingwei Lyu, Yilei Wang

Abstract: Quantum-inspired models have shown enhanced capabilities in various language tasks, including question answering and sentiment analysis. However, current complex-valued-based models primarily focus on sentence embedding, overlooking the significance of the quantum evolution process and the extra time cost incurred by complex expressions. In this work, we present a novel quantum-inspired neural network, SSS-QNN, which integrates the Stochastic Liouville-von Neumann Equation (SLE) to simulate the evolution process and the complex-valued simple recurrent unit (SRU) to reduce the time cost, offering the model physical meaning, thus enhancing the interpretability. We conduct comprehensive experiments on both sentence-level and document-level sentiment classification datasets. Compared to traditional models, large language models, and quantum-inspired models, SSS-QNN demonstrates competitive performance in accuracy and time cost. Additional ablation tests verify the effectiveness of the proposed modules.