Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

DOI

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.

Identifier
DOI https://doi.org/10.18419/darus-3445
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3445
Provenance
Creator Mandl, Alexander ORCID logo; Barzen, Johanna ORCID logo; Leymann, Frank (ORCID: 0000-0002-9123-259X); Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel ORCID logo; Winterhalter, Felix
Publisher DaRUS
Contributor Mandl, Alexander
Publication Year 2023
Funding Reference BMWK 01MK20005N ; BMWK 01MQ22007B
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Mandl, Alexander (University of Stuttgart, Institute of Architecture of Application Systems)
Representation
Resource Type Dataset
Format text/x-python; text/markdown; text/plain
Size 2331; 1586; 3882; 273; 18900; 14704; 13526; 4734; 826; 2284; 2240; 14110; 567; 4469; 1815; 109; 5280; 2935
Version 1.0
Discipline Other