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Software Name: Python Software Name: Numpy

1 to 5 of 5 Results

Reproduction Code 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, "Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/darus-3445, DaRUS, V1 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...
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 re...
Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5 Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification" Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5", https://doi.org/10.18419/darus-3154, DaRUS, V1 Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization.
Data for: On the role of density-driven dissolution of CO2 in karstification Dec 2, 2021 - Density-driven dissolution of CO2 in karst water Class, Holger; Bürkle, Pascal; Trötschler, Oliver; Zimmer, Martin; Strauch, Bettina, 2021, "Data for: On the role of density-driven dissolution of CO2 in karstification", https://doi.org/10.18419/darus-2040, DaRUS, V1, UNF:6:MLmw+UrdmPEjg9Db5HtPDw== [fileUNF] Summary: Data of the "density-driven dissolution of CO2 in karst water" column experiment. A laboratory column was filled with tapwater (water level 5,55 m) and exposed it to an elevated gaseous CO2-concentration, roughly 50 times the current atmospheric concentration (20000 +/-...
Supplementary material for 'Adsorption of Light Gases in Covalent Organic Frameworks: Comparison of Classical Density Functional Theory and Grand Canonical Monte Carlo Simulations' Jul 12, 2021 - Institute of Thermodynamics and Thermal Process Engineering Kessler, Christopher; Eller, Johannes; Gross, Joachim; Hansen, Niels, 2021, "Supplementary material for 'Adsorption of Light Gases in Covalent Organic Frameworks: Comparison of Classical Density Functional Theory and Grand Canonical Monte Carlo Simulations'", https://doi.org/10.18419/darus-1775, DaRUS, V1, UNF:6:kTnkyWKU1qxxwfkXXMvp8w== [fileUNF] This dataset contains results from classical Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) Simulation. We report excellent agreement between the fluid theoretical approach (DFT), which is a more coarse grained approach and stochastic simulation (GCMC) on...

Reproduction Code 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, "Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/darus-3445, DaRUS, V1

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...

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 re...

Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5

Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification"

Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5", https://doi.org/10.18419/darus-3154, DaRUS, V1

Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization.

Data for: On the role of density-driven dissolution of CO2 in karstification

Dec 2, 2021 - Density-driven dissolution of CO2 in karst water

Class, Holger; Bürkle, Pascal; Trötschler, Oliver; Zimmer, Martin; Strauch, Bettina, 2021, "Data for: On the role of density-driven dissolution of CO2 in karstification", https://doi.org/10.18419/darus-2040, DaRUS, V1, UNF:6:MLmw+UrdmPEjg9Db5HtPDw== [fileUNF]

Summary: Data of the "density-driven dissolution of CO2 in karst water" column experiment. A laboratory column was filled with tapwater (water level 5,55 m) and exposed it to an elevated gaseous CO2-concentration, roughly 50 times the current atmospheric concentration (20000 +/-...

Supplementary material for 'Adsorption of Light Gases in Covalent Organic Frameworks: Comparison of Classical Density Functional Theory and Grand Canonical Monte Carlo Simulations'

Jul 12, 2021 - Institute of Thermodynamics and Thermal Process Engineering

Kessler, Christopher; Eller, Johannes; Gross, Joachim; Hansen, Niels, 2021, "Supplementary material for 'Adsorption of Light Gases in Covalent Organic Frameworks: Comparison of Classical Density Functional Theory and Grand Canonical Monte Carlo Simulations'", https://doi.org/10.18419/darus-1775, DaRUS, V1, UNF:6:kTnkyWKU1qxxwfkXXMvp8w== [fileUNF]

This dataset contains results from classical Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) Simulation. We report excellent agreement between the fluid theoretical approach (DFT), which is a more coarse grained approach and stochastic simulation (GCMC) on...

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