Replication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables. This is the version of the code that was used to generate the experiment results in the related publication.

Experiments: - exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Schmidt rank with fixed vector as Schmidt basis state. Varies the associated Schmidt coefficient. - exp_inf_random.py: Trains QNNs using random training data.

Experiment results: - exp_inf_coeffvariation.zip and exp_inf_random.zip contain the raw experiment results for both experiments. - For each combination of controlled variables there is one directory containing the result of all 20 runs of the training process. - The results for each run are comprised of 3 files:   - [id]_losses.npy: The loss during the training process   - [id]_params.npy: The parameters of the QNN after the training process.   - [id]_V.npy: The trained QNN exported as a 2^4 * 2^4 unitary matrix.

Analysis of data (data_extraction.py): - Computes means and standard deviation of various risk measures and saves the results

Plots (plot_obs_risk.py): - Plots the risk w.r.t. the observable for both experiments based on the analysed data obtained from data_extraction.py. - Generates plot_coeffvariation.pdf and plot_random.pdf.