This repository showcases the application of Quantum Machine Learning (QML) to traditional machine learning tasks, such as classification and regression. The purpose is twofold:
The goal of this repository is twofold:
- To explore and advance understanding in state-of-the-art Quantum Machine Learning techniques.
- To demonstrate my expertise in Quantum Machine Learning as a foundation for future career opportunities.
This repository currently includes the following areas of focus:
- Classification:
- Implementations include multiclass classification models using both fully quantum classifiers and hybrid quantum-classical models.
- Regression:
- We explore quantum algorithms for regression using classical datasets, with implementations of both fully quantum and hybrid quantum-classical regressors.
Planned additions and improvements include:
- Development of QML algorithms focused on Natural Language Processing (NLP).
- Exploration and implementation of Quantum Reinforcement Learning models.
- Performance Evaluation: Comparative analysis of quantum versus classical algorithms to assess potential advantages in terms of accuracy, computational complexity, and scalability.
- qiskit >=1.2
- qiskit-algorithms >=0.3
- qiskit-nature >=0.7
- qiskit-machine-learning >=0.7
- qiskit-optimization >=0.6
- scikit-learn >=1.5
- torch >=2.4
- matplotlib >=3.9
- seaborn >=3.9
- pandas >=2.2