A Comparative Implementation of Iris Flower Classification Across Platform

A Comparative Implementation of Iris Flower Classification Across Platform

Mr. Shivanand Maradi1, Abhishek K C2, Adarsh B3, Ashik K A4, Darshan G5
1Guide, 2Final year Student, 3Final year Student, 4Final year Student, 5Final year Student,

Department Of Computer Science And Engineering P E S Institute of Technology and Management, Shimoga

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Abstract - 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.

Key Words: Iris Flower Classification, Machine Learning, Classical Computing, Cloud Computing, Quantum Computing, Qiskit, Google Colab, Quantum Machine Learning, Comparative Analysis