Privacy-Preserving Quantum Natural Language Processing for Secure Text Classification

With the rise of Quantum Natural Language Processing (QNLP) for complex language tasks, ensuring data privacy and security has become a critical challenge. Traditional NLP relies on vast sensitive textual data, posing risks such as data leakage and compliance issues. While quantum computing enhances efficiency and expressiveness, it also introduces new privacy concerns. This paper explores the intersection of QNLP and privacy-preserving techniques, examining vulnerabilities, adapting classical privacy methods to quantum settings, and investigating emerging quantum-based solutions. By addressing key challenges, we aim to bridge the gap between quantum advancements and secure NLP, fostering robust and privacy-compliant QNLP models.