The rise of Artificial Intelligence (AI) and Machine Learning (ML) has created a need for applications that leverage Quantum Computing and Explainable Artificial Intelligence (XAI) to explain complex “black-box” models. In this project, an iris classifier is developed using a two-qubit Quantum Machine Learning (QML) model and then explained using Local interpretable model-agnostic explanations (Lime) and Diverse Counterfactual Explanations (DiCE). Through testing and analysis, future improvements and related research are suggested to minimise discrepancies and reduce runtime complexity. The implementation developed may be on a smaller scale compared to larger real-world applications. However, there is no doubt that we will eventually reach the true potential of XAI with QML and use it to solve more notable problems that classical computers cannot solve.