Quantum Computing @IAAS

Dataverse for all quantum computing projects of the Institute of Architecture of Application Systems (IAAS)

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61 to 70 of 98 Results

quantum_gates.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 4.4 KB - MD5: 880496a1590c56ea84ba76c2c826db7a Gate implementations of common quantum gates for simulation.
README.md Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Markdown Text - 1.8 KB - MD5: 031d17f5835f39bb778cc86ddb5a9873 Readme file with additional information.
requirements.txt Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Plain Text - 109 B - MD5: fa967312ca664d7446bf755b0315efef Python requirements for reproduction.
utils.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 5.2 KB - MD5: 1f7467697f67061ced50e55509ee2058 Utility functions for data generation and experiment setup.
vis_utils.py Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs Python Source Code - 2.9 KB - MD5: e03fd7afffe888e2895e000bc5d68279 Utility functions for visualisation.
average_rank_results.zip Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs ZIP Archive - 732.9 MB - MD5: 4b44f0e1d48bfd7ecd536d016b120dd1 Raw data (losses and risks) for training QNNs using entangled training data with varying degree of entanglement. Directory t[a]r[b] contains results for QNNs trained with "a" training samples of average Schmidt rank "b". For analyzed results see avg_rank_risks.npy.
avg_rank_experiments.pdf Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs Adobe PDF - 70.2 KB - MD5: 21850b0dc649f6572df2431daf6e5c6f The average risk after training a 6-qubit QNN for randomly generated target unitaries. For this experiment, data of varying Schmidt rank with average ranks r ∈ {1,2,4,64} and different input sizes t ∈ {1,2,4,8,16,32,64} was used. The markers give the experimentally computed average risk for each pair of Schmidt rank and training set size. The lower...
avg_rank_losses.npy Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs Unknown - 1.0 KB - MD5: 3ab5355e889b0bdc4038631e8198b157 Map (r: int-> (t: int -> l: float)) of average final losses "l" after training of QNNs with "t" entangled datasets of average Schmidt rank "r".
avg_rank_losses.pdf Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs Adobe PDF - 158.8 KB - MD5: e2e6fbb55a2e0cb2f2fd0c46f3086cc4 The average losses at the end of training for QNNs using training data of varying Schmidt ranks with mean Schmidt ranks in {1,2,4,64}.
avg_rank_risks.npy Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs Unknown - 618 B - MD5: 64cd3be5713e8bbe896a1234fd657aef Map (r: int-> (t: int -> R: float)) of average final risks "R" after training of QNNs with "t" entangled datasets of average Schmidt rank "r".

quantum_gates.py

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

Python Source Code - 4.4 KB -

Gate implementations of common quantum gates for simulation.

README.md

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

Markdown Text - 1.8 KB -

Readme file with additional information.

requirements.txt

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

Plain Text - 109 B -

Python requirements for reproduction.

utils.py

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

Python Source Code - 5.2 KB -

Utility functions for data generation and experiment setup.

vis_utils.py

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

Python Source Code - 2.9 KB -

Utility functions for visualisation.

average_rank_results.zip

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

ZIP Archive - 732.9 MB -

Raw data (losses and risks) for training QNNs using entangled training data with varying degree of entanglement. Directory t[a]r[b] contains results for QNNs trained with "a" training samples of average Schmidt rank "b". For analyzed results see avg_rank_risks.npy.

avg_rank_experiments.pdf

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

Adobe PDF - 70.2 KB -

The average risk after training a 6-qubit QNN for randomly generated target unitaries. For this experiment, data of varying Schmidt rank with average ranks r ∈ {1,2,4,64} and different input sizes t ∈ {1,2,4,8,16,32,64} was used. The markers give the experimentally computed average risk for each pair of Schmidt rank and training set size. The lower...

avg_rank_losses.npy

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

Unknown - 1.0 KB -

Map (r: int-> (t: int -> l: float)) of average final losses "l" after training of QNNs with "t" entangled datasets of average Schmidt rank "r".

avg_rank_losses.pdf

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

Adobe PDF - 158.8 KB -

The average losses at the end of training for QNNs using training data of varying Schmidt ranks with mean Schmidt ranks in {1,2,4,64}.

avg_rank_risks.npy

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

Unknown - 618 B -

Map (r: int-> (t: int -> R: float)) of average final risks "R" after training of QNNs with "t" entangled datasets of average Schmidt rank "r".

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