Institute for Architecture for Application Systems

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License: MIT License

1 to 10 of 18 Results

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
nlihx_exp.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 2.2 KB - MD5: 6bafb223195f04e5747c244b074eda3e Experiments for training QNNs using linearly dependent data.

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

nlihx_exp.py

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

Python Source Code - 2.2 KB -

Experiments for training QNNs using linearly dependent data.

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