%0 Generic %1 mandl2024repository %A Mandl, Alexander %A Bechtold, Marvin %A Barzen, Johanna %A Leymann, Frank %D 2024 %K darus ubs_10005 ubs_20008 ubs_30079 ubs_40104 unibibliografie %R 10.18419/darus-4113 %T Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" %X 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.

@misc{mandl2024repository, abstract = {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. }, added-at = {2024-10-15T10:13:37.000+0200}, affiliation = {Mandl, Alexander/University of Stuttgart, Bechtold, Marvin/University of Stuttgart, Barzen, Johanna/University of Stuttgart, Leymann, Frank/University of Stuttgart}, author = {Mandl, Alexander and Bechtold, Marvin and Barzen, Johanna and Leymann, Frank}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21cb0bc90345f26cc32705f0903a6ec60/unibiblio}, doi = {10.18419/darus-4113}, howpublished = {Software}, interhash = {e4f1c8fd8ef991e9ccd7dbc7bf6b1820}, intrahash = {1cb0bc90345f26cc32705f0903a6ec60}, keywords = {darus ubs_10005 ubs_20008 ubs_30079 ubs_40104 unibibliografie}, note = {Related to: Mandl, Alexander; Barzen, Johanna; Bechtold, Marvin; Leymann, Frank, 2024, "Minimial-Risk Training Samples for QNN Training from Measurements"}, orcid-numbers = {Mandl, Alexander/0000-0003-4502-6119, Bechtold, Marvin/0000-0002-7770-7296, Barzen, Johanna/0000-0001-8397-7973, Leymann, Frank/0000-0002-9123-259X}, timestamp = {2025-03-11T10:08:58.000+0100}, title = {Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"}, year = 2024 }