Institute for Architecture for Application Systems

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

2,391 to 2,400 of 2,462 Results

plots.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 13.8 KB - MD5: 8fa54e027cd52ee10a990216bd0b3e04 Python script for generating the plots for the quantum risk for all experiments and for aggregating and analyzing the raw experiment results
qnn.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 567 B - MD5: 2d59023ea07e02020019970d499218fa General QNN description
quantum_gates.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 4.4 KB - MD5: 880496a1590c56ea84ba76c2c826db7a Gate implementations of common quantum gates for simulation.
README.md Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Markdown Text - 1.8 KB - MD5: 031d17f5835f39bb778cc86ddb5a9873 Readme file with additional information.
requirements.txt Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Plain Text - 109 B - MD5: fa967312ca664d7446bf755b0315efef Python requirements for reproduction.
utils.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 5.2 KB - MD5: 1f7467697f67061ced50e55509ee2058 Utility functions for data generation and experiment setup.
vis_utils.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 2.9 KB - MD5: e03fd7afffe888e2895e000bc5d68279 Utility functions for visualisation.
Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs Sep 27, 2023 - Quantum Computing @IAAS Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/DARUS-3442, DaRUS, V1 Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets. The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the related publication. The QNNs are trained with (i) samples of varying Sc...
average_rank_results.zip Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs ZIP Archive - 732.9 MB - MD5: 4b44f0e1d48bfd7ecd536d016b120dd1 Raw data (losses and risks) for training QNNs using entangled training data with varying degree of entanglement. Directory t[a]r[b] contains results for QNNs trained with "a" training samples of average Schmidt rank "b". For analyzed results see avg_rank_risks.npy.
avg_rank_experiments.pdf Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs Adobe PDF - 70.2 KB - MD5: 21850b0dc649f6572df2431daf6e5c6f The average risk after training a 6-qubit QNN for randomly generated target unitaries. For this experiment, data of varying Schmidt rank with average ranks r ∈ {1,2,4,64} and different input sizes t ∈ {1,2,4,8,16,32,64} was used. The markers give the experimentally computed average risk for each pair of Schmidt rank and training set size. The lower...

plots.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 13.8 KB -

Python script for generating the plots for the quantum risk for all experiments and for aggregating and analyzing the raw experiment results

qnn.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 567 B -

General QNN description

quantum_gates.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 4.4 KB -

Gate implementations of common quantum gates for simulation.

README.md

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Markdown Text - 1.8 KB -

Readme file with additional information.

requirements.txt

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Plain Text - 109 B -

Python requirements for reproduction.

utils.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 5.2 KB -

Utility functions for data generation and experiment setup.

vis_utils.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 2.9 KB -

Utility functions for visualisation.

Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

Sep 27, 2023 - Quantum Computing @IAAS

Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/DARUS-3442, DaRUS, V1

Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets. The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the related publication. The QNNs are trained with (i) samples of varying Sc...

average_rank_results.zip

Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

ZIP Archive - 732.9 MB -

Raw data (losses and risks) for training QNNs using entangled training data with varying degree of entanglement. Directory t[a]r[b] contains results for QNNs trained with "a" training samples of average Schmidt rank "b". For analyzed results see avg_rank_risks.npy.

avg_rank_experiments.pdf

Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

Adobe PDF - 70.2 KB -

The average risk after training a 6-qubit QNN for randomly generated target unitaries. For this experiment, data of varying Schmidt rank with average ranks r ∈ {1,2,4,64} and different input sizes t ∈ {1,2,4,8,16,32,64} was used. The markers give the experimentally computed average risk for each pair of Schmidt rank and training set size. The lower...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications