Quantum-inspired neural networks with stochastic dynamics for multimodal sentiment analysis and sarcasm detection

Quantum-inspired neural networks have demonstrated strong potential in modeling non-classical phenomena in cognitive tasks, particularly in multimodal sentiment analysis, marking a significant advancement over traditional models. However, existing multimodal quantum-inspired neural networks fall short in fully modeling the multimodal density matrix, typically relying on simplistic neural mappings to represent quantum entanglement. This lack of explicit physical constraints, particularly those governing open quantum system dynamics, limits both the interpretability and performance. To address this limitation, we propose a novel framework grounded in quantum stochastic dynamics, introducing two quantum-inspired neural networks, which model the evolution of multimodal data as Markovian and non-Markovian open quantum systems, respectively. This approach enables the simulation of quantum system evolution to capture rich non-classical interactions between modalities. The resulting entangled multimodal density matrix is then measured through quantum projections to extract high-level features for downstream sentiment analysis and sarcasm detection. Extensive experiments on benchmark bimodal and trimodal datasets demonstrate that our models consistently outperform state-of-the-art traditional baselines, large-scale language models and quantum-inspired neural networks. Ablation studies confirm the critical role of quantum stochastic dynamics in performance gains. Furthermore, we enhance the interpretability by tracking the evolution of the density matrix using von-Neumann entanglement entropy as a quantitative metric, providing deeper insight into the internal mechanisms of the model.