Quantum computing (QC) is an advancing discipline that exploits the power of quantum physics to tackle complex problems that are difficult for traditional computers. Machine learning (ML) is a distinct field of study in artificial intelligence that allows computers to gain information and recognize patterns by analyzing previous experiences. Due to the constant expansion of data, machine learning approaches may become insufficient for managing large amounts of data, while quantum computing provides the promise for faster processing powers. Quantum machine learning (QML) is a field that has evolved from the combination of QC with ML. QML approaches use the high-speed computing capabilities of QC, leading to faster performance compared to their traditional counterparts. Natural language processing (NLP) is a subfield of artificial intelligence that enables computers to understand human languages. Presently, researchers are striving to use the enhanced computational skills of QML in the domain of NLP. This research paper presents a comprehensive experimental examination of several quantum QML algorithms used for tagging the part-of-speech of a given phrase. The QML approaches being considered are QRNN and QLSTM. The experimental analysis involves evaluating performance metrics such as precision, recall, and F1-score. The findings provide a comprehensive study of both QML models.