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Publication Year: 2023 License: MIT License

1 to 10 of 304 Results

ABxM.PlateStructures-1.1.2-UML.pdf Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures Adobe PDF - 90.0 KB - MD5: 2bfc2380fd05012b3a984dacd172c7ec
ABxM.PlateStructures-1.1.2.zip Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures ZIP Archive - 2.0 MB - MD5: a0f0f31b82f954e6f338dc50f17ca6ea
ABxM.PlateStructuresCore.dll Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures Windows Executable - 64.0 KB - MD5: 18245701ac82c494ae0c1230304055ba
ABxM.PlateStructures.gha Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures Unknown - 244.0 KB - MD5: 57bd3f01255cad70cd10615cbb1d1d43
Release_notes.md Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures Markdown Text - 914 B - MD5: 4febe5d48b52c949847284e4013f94f6
avg_rank_exp.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 2.3 KB - MD5: 3092220f87583ca17e7cfd73fdacafef Experiments for training QNNs using training data of varying Schmidt rank.
classic_training.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 1.5 KB - MD5: ff77e11ba2a65fbf30afe3680e7903e6 Cost function and training routines procedures for PyTorch QNN simulation.
config.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 3.8 KB - MD5: b0b7ff540bb8e1b631cff61a652b06f7 Configuration structures for experiments.
cost_modifying_functions.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 273 B - MD5: b1a0c53c5525b9980bb82bf3784e3c7a Various functions to modify the loss function after evaluation.
cuda_qnn.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 18.5 KB - MD5: ee5babfdaf1828206a38944c1f26c713 QNN ansatz implementations for classical simulation using PyTorch.

ABxM.PlateStructures-1.1.2-UML.pdf

Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures

Adobe PDF - 90.0 KB -

ABxM.PlateStructures-1.1.2.zip

Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures

ZIP Archive - 2.0 MB -

ABxM.PlateStructuresCore.dll

Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures

Windows Executable - 64.0 KB -

ABxM.PlateStructures.gha

Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures

Unknown - 244.0 KB -

Release_notes.md

Dec 11, 2023 - ABxM.PlateStructures: Agent-based Architectural Design of Plate Structures

Markdown Text - 914 B -

avg_rank_exp.py

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

Python Source Code - 2.3 KB -

Experiments for training QNNs using training data of varying Schmidt rank.

classic_training.py

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

Python Source Code - 1.5 KB -

Cost function and training routines procedures for PyTorch QNN simulation.

config.py

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

Python Source Code - 3.8 KB -

Configuration structures for experiments.

cost_modifying_functions.py

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

Python Source Code - 273 B -

Various functions to modify the loss function after evaluation.

cuda_qnn.py

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

Python Source Code - 18.5 KB -

QNN ansatz implementations for classical simulation using PyTorch.

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