Iris Flower Classification

This paper presents a comparative analysis of the Iris Flower Classification problem implemented across three distinct computational platforms: classical computing using Jupyter Notebook, cloud computing via Google Colab, and quantum computing leveraging IBM Qiskit. The study evaluates the platforms based on execution efficiency, classification accuracy, and scalability. While classical and cloud-based implementations yielded similar performance metrics, the quantum approach demonstrated unique computational characteristics and challenges, such as noise and resource limitations. This research highlights the growing potential of quantum computing for machine learning applications and provides a foundation for future advancements in integrating quantum techniques with traditional methodologies.