Quantum Computing @IAAS

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Publication Year: 2023

1 to 10 of 55 Results

Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Sep 27, 2023 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...
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.
data.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 14.4 KB - MD5: 05a60d0e08fe086be30c1982b88ee9a8 Data generation routines for the experiments.
generate_experiments.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 13.2 KB - MD5: 27ae2978ce34969769e840b21da5526c Experiment entry point. Generates training data, calls simulation and training routines and saves results. Also responsible for distributing experiment workloads to multiple processors.
logger.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 4.6 KB - MD5: 7cad64b10f13aa99ad9bec3335e7a72a Logging utilities
metrics.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 826 B - MD5: dc804c83ce2807ae1ab4dd51b1c87148 Functions for evaluating the quantum risk

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

Sep 27, 2023

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...

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.

data.py

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

Python Source Code - 14.4 KB -

Data generation routines for the experiments.

generate_experiments.py

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

Python Source Code - 13.2 KB -

Experiment entry point. Generates training data, calls simulation and training routines and saves results. Also responsible for distributing experiment workloads to multiple processors.

logger.py

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

Python Source Code - 4.6 KB -

Logging utilities

metrics.py

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

Python Source Code - 826 B -

Functions for evaluating the quantum risk

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