Our latest work has shown promising results for a quantum-native text classification algorithm. The model exploits a novel encoding procedure to represent inputs, and has been tested on a sentiment analysis task. In this project we expand the scope by evaluating the model on different tasks, seek ways to extend it beyond binary classification, and possibly evaluate its performance on quantum hardware. This thesis requires a background in quantum computing / quantum information theory.