Quantum computing has emerged as a revolutionary field with the potential to revolutionize computation and solve complex problems that are beyond the |capabilities of classical computers. This project presents a comparative study of Quantum Naive Bayes and Classical Gaussian Naive Bayes algorithms applied to the NASA Nearest Earth Object Dataset. Quantum computing, implemented using the Qiskit library, is explored alongside classical machine learning for classification tasks.
In our final year capstone project, we investigate the performance of Quantum Naive Bayes, implemented with help of Qiskit, and Classical Gaussian Naive Bayes on the NASA Nearest Earth Object Dataset. The project aims to understand the comparative strengths and weaknesses of quantum and classical machine learning approaches for classification tasks in the realm of space science.
To get started with this project, follow these steps:
- Clone this repository to your local machine.
- Install the required libraries using
pip install -r requirements.txt. - Run the main.py file on a google colab.
Yet to write
- Mausmi Sinha
- Aman Singh Bhogal
This project is licensed under the MIT License. See the LICENSE file for details.
We would like to express our gratitude to Prof. Pratiksha Meshram at SVKM's NMIMS MPSTME Shirpur Campus for her guidance and support throughout the project.
For questions, feedback, or collaboration opportunities, please contact:
- Mausmi Sinha - [sinhatanu2001@gmail.com] - GitHub
- Aman Singh Bhogal - [bhogalamansingh22@gmail.com] - GitHub