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The role of data embedding in quantum autoencoders for improved anomaly detection

arxiv DOI

Datasets

  • The credit card fraud dataset has been taken from Kaggle and preprocessed using sklearn.preprocessing.MinMaxScaler(feature_range=(-np.pi, np.pi)).

  • Pedregosa, F., Varoquaux, Gael, Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830.

Usage

The code to reproduce the results is available in qVAE.py, and all necessary dependencies can be installed using the requirements.txt file. Please note that the package versions are fixed to ensure complete reproducibility. For a list of execution options, run ./qVAE.py -h.