Implement variational quantum classifier on heart attack data available on kaggle with the aim to understand different variational models and different feature maps
- Create a virtual environment:
python3 -m venv venv- Activate the environment:
source venv/bin/activate- Update
pip:
pip install --upgrade pip- Install requirements
pip install -r requirements.txt- Clone the repo
git clone https://github.com/0x6f736f646f/variational-quantum-classifier-on-heartattack- Launch Jupyter Notebook:
jupyter notebook
- Open and run the jupyter notebooks under
Src/Notebooks
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01dataExploration.ipynb Data exploration
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02-pennylane.ipynb Pennylane version of VQE
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02-qiskit.ipynb Qiskit version of VQE implementation
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03-analyseBenchmarks.ipynb Analysing benchmarks
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04generateImages.ipynb Notebook to generate images
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05cleanwinedata.ipynb Cleaning wine data
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06validatewine.ipynb Validating wine data
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07cleanirisdata.ipynb Cleaning iris data
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08validateiris.ipynb Validating iris data
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09analysewine.ipynb Analyse wine data
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09generalisation.ipynb Generalisation of all models
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10analyseiris.ipynb Analyse iris data
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.