Quantum Computing @IAAS

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

31 to 40 of 98 Results

qnn.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 567 B - MD5: 2d59023ea07e02020019970d499218fa Structure of QNN implementations.
quantum_gates.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 4.4 KB - MD5: 880496a1590c56ea84ba76c2c826db7a Collection of various quantum gates implemented using pytorch for the QNN implementations.
requirements.txt Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Plain Text - 113 B - MD5: 2afe47d43d99e1cebff654aec793343a Python requirements for experiments.
risk.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 3.5 KB - MD5: 451c38f7f182216209d265037c71722a Utility functions for computing risk.
sampling_error_extension.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 9.8 KB - MD5: fca958b2e95c257b8cebd29fc81b41fd Functions/Classes required for training with observables/training by sampling from measurements.
unitaries.zip Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" ZIP Archive - 43.6 KB - MD5: 364ec719013af5ad6c6fc07d236211ad Unitary matrices (as pytorch files) that were used as target operators in the experiments.
utils.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 5.2 KB - MD5: a5623fd0ce92b1547522e599c9fc5ec9 General utility functions.
data.tab Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States Tabular Data - 58.2 MB - 14 Variables, 300000 Observations - UNF:6:79HIPgCvMDi51TZ2V7NUew== Generated data. A detailed description of the columns can be found in the README.md file.
experiment_plot_error.pdf Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States Adobe PDF - 196.7 KB - MD5: 01650ac972958f9fe0687708d2fb0990 Figure 6: Plot from the generated data.
experiment.py Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States Python Source Code - 5.6 KB - MD5: fb27515fd970f4ae24ec784a95135668 Functions to execute the experiment.

qnn.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 567 B -

Structure of QNN implementations.

quantum_gates.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 4.4 KB -

Collection of various quantum gates implemented using pytorch for the QNN implementations.

requirements.txt

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Plain Text - 113 B -

Python requirements for experiments.

risk.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 3.5 KB -

Utility functions for computing risk.

sampling_error_extension.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 9.8 KB -

Functions/Classes required for training with observables/training by sampling from measurements.

unitaries.zip

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

ZIP Archive - 43.6 KB -

Unitary matrices (as pytorch files) that were used as target operators in the experiments.

utils.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 5.2 KB -

General utility functions.

data.tab

Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States

Tabular Data - 58.2 MB - 14 Variables, 300000 Observations -

Generated data. A detailed description of the columns can be found in the README.md file.

experiment_plot_error.pdf

Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States

Adobe PDF - 196.7 KB -

Figure 6: Plot from the generated data.

experiment.py

Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States

Python Source Code - 5.6 KB -

Functions to execute the experiment.

Add Data

Share Dataverse

Link Dataverse

Reset Modifications