Advancing Textual Semantic Understanding with Quantum LSTM Networks

Long Short-Term Memory (LSTM) networks are adept at managing long-term dependencies in sequential data, making them particularly effective for understanding semantics in context. However, challenges such as language complexity and data limitations persist in natural language processing (NLP). Quantum computing utilizes quantum mechanics to tackle complex language structures with limited data. By integrating quantum computing with LSTM, we can address the issues of linguistic complexity and data scarcity. In this paper, we present a Quantum Long Short-Term Memory (QLSTM) network that comprehends the semantics of textual data. Our simulations demonstrate superior capability of QLSTM in capturing textual semantics.