Tamil Named Entity Recognition Using Classical Machine Learning, Quantum Feature Processing, and a Simplified QNLP-Inspired Component

Abstract: Tamil Named Entity Recognition (NER) is an important natural language processing task for identifying persons, organizations, and locations in Tamil text. This paper presents a preliminary integrated workflow combining classical machine learning (ML), quantum machine learning (QML)-inspired feature processing, and a simplified quantum natural language processing (QNLP)-inspired component. Tamil text was processed using XLM-RoBERTa, and the resulting representations were reduced using Principal Component Analysis. Classical features, simulated quantum features, and a simplified QNLP-inspired representation were evaluated with logistic-regression classifiers. In the prototype experiment, the classical baseline achieved the highest accuracy and weighted F1-score. The results demonstrate the feasibility of constructing a hybrid workflow, but they do not establish quantum advantage. The quantum circuit used fixed random parameters, and the QNLP component was a simplified proof-of-concept representation.

Keywords: Tamil NLP, Named Entity Recognition, Machine Learning, Quantum Machine Learning, Quantum Natural Language Processing, XLM-RoBERTa