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https://github.com/rodneyosodo/variational-quantum-classifier-on-heartattack
Implement variational quantum classifier on heart attack data available on kaggle with the aim to understand different variational models and different feature maps
https://github.com/rodneyosodo/variational-quantum-classifier-on-heartattack
machine-learning qiskit quantum-computing quantum-machine-learning
Last synced: 9 days ago
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Implement variational quantum classifier on heart attack data available on kaggle with the aim to understand different variational models and different feature maps
- Host: GitHub
- URL: https://github.com/rodneyosodo/variational-quantum-classifier-on-heartattack
- Owner: rodneyosodo
- License: mit
- Created: 2020-11-11T13:11:08.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-28T18:28:27.000Z (over 3 years ago)
- Last Synced: 2024-10-11T14:10:49.311Z (26 days ago)
- Topics: machine-learning, qiskit, quantum-computing, quantum-machine-learning
- Language: Jupyter Notebook
- Homepage: https://rodneyosodo.medium.com/qa1-my-quantum-open-source-foundation-project-402d4f1df1a5
- Size: 17.8 MB
- Stars: 19
- Watchers: 1
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# variational-quantum-classifier-on-heartattack
Implement variational quantum classifier on heart attack data available on kaggle with the aim to understand different variational models and different feature maps
## Running the notebooks
1. Create a virtual environment:
```bash
python3 -m venv venv
```
2. Activate the environment:
```bash
source venv/bin/activate
```
3. Update `pip`:
```bash
pip install --upgrade pip
```
4. Install requirements
```bash
pip install -r requirements.txt
```
5. Clone the repo
```bash
git clone https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack
```
6. Launch Jupyter Notebook:
```
jupyter notebook
```
7. Open and run the jupyter notebooks under `Src/Notebooks`
## Project Structure
1. [01dataExploration.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/01dataExploration.ipynb) Data exploration
2. [02-pennylane.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/02-pennylane.ipynb) Pennylane version of VQE
3. [02-qiskit.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/02-qiskit.ipynb) Qiskit version of VQE implementation
4. [03-analyseBenchmarks.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/03-analyseBenchmarks.ipynb) Analysing benchmarks
5. [04generateImages.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/04generateImages.ipynb) Notebook to generate images
6. [05cleanwinedata.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/05cleanwinedata.ipynb) Cleaning wine data
7. [06validatewine.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/06validatewine.ipynb) Validating wine data
8. [07cleanirisdata.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/07cleanirisdata.ipynb) Cleaning iris data
9. [08validateiris.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/08validateiris.ipynb) Validating iris data
10. [09analysewine.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/09analysewine.ipynb) Analyse wine data
11. [09generalisation.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/09generalisation.ipynb) Generalisation of all models
12. [10analyseiris.ipynb](https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack/blob/main/Src/Notebooks/10analyseiris.ipynb) Analyse iris data
## Acknowledgements
I would like to thank my mentor **Amira Abbas** for her constant support and guidance without who this project wouldn't have taken shape. I would also like to thank the **QOSF** team for giving me this fantastic opportunity of being a part of an awesome mentorship program.