%0 Generic %1 mandl2023reproduction %A Mandl, Alexander %A Barzen, Johanna %A Leymann, Frank %A Mangold, Victoria %A Riegel, Benedikt %A Vietz, Daniel %A Winterhalter, Felix %D 2023 %K %R 10.18419/darus-3445 %T Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs : Constraints on the Linear Structure of the Training Data %X Replication code for training Quantum Neural Networks using entangled datasets. This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see the Github repository. Experiments: avg_rank_exp.py: Experiments for training QNNs using training data of varying Schmidt rank. nlihx_exp.py: Experiments for training QNNs using linearly dependent data. ortho_exp.py: Experiments for training QNNs using orthogonal training data. Visualisation/Analysis of data (plots.py):- Generates plots for the experiments above either from the data in experimental_results or from the processed results (see Data).- Processes results to extract information from raw data in experimental_results (to change behavior see the function calls at the end of plots.py). Data: The raw data for the experiments is available in the experiment dataset.

@misc{mandl2023reproduction, abstract = {Replication code for training Quantum Neural Networks using entangled datasets. This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see the Github repository. Experiments: avg_rank_exp.py: Experiments for training QNNs using training data of varying Schmidt rank. nlihx_exp.py: Experiments for training QNNs using linearly dependent data. ortho_exp.py: Experiments for training QNNs using orthogonal training data. Visualisation/Analysis of data (plots.py):- Generates plots for the experiments above either from the data in experimental_results or from the processed results (see Data).- Processes results to extract information from raw data in experimental_results (to change behavior see the function calls at the end of plots.py). Data: The raw data for the experiments is available in the experiment dataset. }, added-at = {2023-10-04T12:20:41.000+0200}, affiliation = {Mandl, Alexander/University of Stuttgart, Institute of Architecture of Application Systems, Barzen, Johanna/University of Stuttgart, Institute of Architecture of Application Systems, Leymann, Frank/University of Stuttgart, Institute of Architecture of Application Systems, Mangold, Victoria/, Riegel, Benedikt/, Vietz, Daniel/University of Stuttgart, Institute of Architecture of Application Systems, Winterhalter, Felix/}, author = {Mandl, Alexander and Barzen, Johanna and Leymann, Frank and Mangold, Victoria and Riegel, Benedikt and Vietz, Daniel and Winterhalter, Felix}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e7ab48768078b10286a57ec071822dc9/unibiblio}, doi = {10.18419/darus-3445}, howpublished = {Software}, interhash = {3e3111bc346e82027e1c60f22c16b1e2}, intrahash = {e7ab48768078b10286a57ec071822dc9}, keywords = {}, orcid-numbers = {Mandl, Alexander/0000-0003-4502-6119, Barzen, Johanna/0000-0001-8397-7973, Leymann, Frank/0000-0002-9123-259X, Vietz, Daniel/0000-0003-1366-5805}, timestamp = {2023-10-04T10:20:41.000+0200}, title = {Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs : Constraints on the Linear Structure of the Training Data}, year = 2023 }