Quantum Natural Language Processing

Quantum Natural Language Processing (QNLP) aims to leverage quantum computing's power to improve natural language processing tasks. Current research focuses on adapting classical NLP models, such as recurrent neural networks and transformers, to quantum architectures, often employing parameterized quantum circuits and quantum tensor networks, and exploring quantum versions of techniques like self-attention and word embeddings. This emerging field shows promise for enhancing the efficiency and interpretability of NLP, particularly in computationally intensive applications like machine translation, sentiment analysis, and protein classification, potentially surpassing the capabilities of classical methods in certain areas.