Quantum Computing @IAAS

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

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31 to 40 of 105 Results

plot_random.pdf Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Adobe PDF - 201.6 KB - MD5: 11f669c77e88e3114732fec1b66cac8c Figure 2: Average risk of QNNs that are trained with training samples of varying Schmidt rank r comprised of randomly sampled Schmidt basis vectors.
pt_train.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 7.9 KB - MD5: 7cf85753480f2bb138f59e5d96735313 The QNN training routines that are used in the experiments.
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 repository for: Cutting a Wire with Non-Maximally Entangled States Jan 29, 2024 Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander, 2024, "Data repository for: Cutting a Wire with Non-Maximally Entangled States", https://doi.org/10.18419/DARUS-3888, DaRUS, V1, UNF:6:79HIPgCvMDi51TZ2V7NUew== [fileUNF] This dataset contains the replication code for the publication titled "Cutting a Wire with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication. For comprehensive instructions on using the provided data and code, please refer to the README...

plot_random.pdf

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

Adobe PDF - 201.6 KB -

Figure 2: Average risk of QNNs that are trained with training samples of varying Schmidt rank r comprised of randomly sampled Schmidt basis vectors.

pt_train.py

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

Python Source Code - 7.9 KB -

The QNN training routines that are used in the experiments.

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 repository for: Cutting a Wire with Non-Maximally Entangled States

Jan 29, 2024

Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander, 2024, "Data repository for: Cutting a Wire with Non-Maximally Entangled States", https://doi.org/10.18419/DARUS-3888, DaRUS, V1, UNF:6:79HIPgCvMDi51TZ2V7NUew== [fileUNF]

This dataset contains the replication code for the publication titled "Cutting a Wire with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication. For comprehensive instructions on using the provided data and code, please refer to the README...

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