Paraphrase Detection is a core task in natural language processing (NLP) that aims to determine whether two sentences convey equivalent meanings. This work proposes a hybrid quantum–classical framework that integrates Sentence-BERT embeddings, simulated quantum feature encoding, and classical machine learning models to enhance semantic similarity detection. Initially, sentence pairs are embedded using Sentence-BERT and standardized through feature scaling. These representations are then transformed via rotation-based quantum circuits to capture higher-order feature interactions and non-linear dependencies. The resulting hybrid feature space, combining classical and quantum-inspired components, is evaluated using LightGBM and deep neural network classifiers. Experimental results show that the hybrid model incorporating quantum-inspired features achieved superior classification performance, yielding a 10% improvement in overall accuracy outperforming standalone deep learning baselines. These findings demonstrate that quantum–classical fusion enhances semantic feature extraction and significantly improves paraphrase detection performance.