Institute for Architecture for Application Systems

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21 to 30 of 2,454 Results

results.zip Dec 9, 2024 - Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation ZIP Archive - 210.5 MB - MD5: 427613443f5e68f44d561e7c1d7b64fe
Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Oct 8, 2024 - Quantum Computing @IAAS Mandl, Alexander; Bechtold, Marvin; Barzen, Johanna; Leymann, Frank, 2024, "Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"", https://doi.org/10.18419/DARUS-4113, DaRUS, V1 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 Sch...
cuda_qnn.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 24.6 KB - MD5: de37947e29cd064e381011cfc7b1f24d Collection of various QNN implementations. For the paper the implementation UnitaryParametrization is used.
data_extended.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 15.3 KB - MD5: 1a8b536beb4f0ff6160a56dbeac17078 Functions for generating training data of various structures.
data_extraction.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 9.8 KB - MD5: 03bb5294f00ed8f2a2cd92b5e22ddb8b Analyzes raw experiment data to compute means and standard deviation of various metrics.
experiment.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 3.7 KB - MD5: 0f05e371ceebb31bc2fb7b37f3c48e65 General experiment code. Trains networks and outputs the trained network.
exp_inf_coeffvariation.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 3.4 KB - MD5: c862a47b55ab80158e9e7aeac7656807 Experiment entry point for the experiment in Section 4.2/Figure 1.
exp_inf_coeffvariation.zip Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" ZIP Archive - 490.5 MB - MD5: 8e6970ba91f0895eecbedc441ad7b7be Raw experiment results for the experiments shown in Figure 1.
exp_inf_random.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 1.4 KB - MD5: 7955b007d3d6cbdaf5b3bbcab20a81c0 Experiment entry point for the experiment in Section 4.3/Figure 2.
exp_inf_random.zip Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" ZIP Archive - 96.7 MB - MD5: 0563f65cfd2bc846aca42515bb09947d Raw experiment results for the experiments shown in Figure 2.

results.zip

Dec 9, 2024 - Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation

ZIP Archive - 210.5 MB -

Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Oct 8, 2024 - Quantum Computing @IAAS

Mandl, Alexander; Bechtold, Marvin; Barzen, Johanna; Leymann, Frank, 2024, "Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"", https://doi.org/10.18419/DARUS-4113, DaRUS, V1

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 Sch...

cuda_qnn.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 24.6 KB -

Collection of various QNN implementations. For the paper the implementation UnitaryParametrization is used.

data_extended.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 15.3 KB -

Functions for generating training data of various structures.

data_extraction.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 9.8 KB -

Analyzes raw experiment data to compute means and standard deviation of various metrics.

experiment.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 3.7 KB -

General experiment code. Trains networks and outputs the trained network.

exp_inf_coeffvariation.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 3.4 KB -

Experiment entry point for the experiment in Section 4.2/Figure 1.

exp_inf_coeffvariation.zip

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

ZIP Archive - 490.5 MB -

Raw experiment results for the experiments shown in Figure 1.

exp_inf_random.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 1.4 KB -

Experiment entry point for the experiment in Section 4.3/Figure 2.

exp_inf_random.zip

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

ZIP Archive - 96.7 MB -

Raw experiment results for the experiments shown in Figure 2.

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