Quantum Computing @IAAS

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11 to 20 of 105 Results

plot_2.pdf Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Adobe PDF - 135.9 KB - MD5: cf95b072843c452a8f6cae28a7ea7141 Plot from the generated data of experiment 1.
plot.ipynb Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Jupyter Notebook - 58.7 KB - MD5: dddcc6cf6126e960de35b77316a6c9dd The main entry point to plot generated data. Requires the data as CSV file.
README.md Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Markdown Text - 2.1 KB - MD5: e578ebbd1e188030418ecb9c26adc624 Readme file with additional information on the data and code.
requirements.txt Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Plain Text - 2.1 KB - MD5: f5d9b3247d64823beabb16315090f872 Python requirements for reproduction.
states.py Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Python Source Code - 1.3 KB - MD5: a0cdda6de40f11db533b691699e3ec80 Functions to generate entangled states
test_cut.py Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Python Source Code - 2.7 KB - MD5: b244c0ef8c8a9c58576370ae90a79590 Tests for functions in "cut.py".
test_mubs.py Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Python Source Code - 1.2 KB - MD5: 2f60a0784ca977a192382cfa97dd44d3 Tests for functions in "mubs.py".
test_operators.py Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States" Python Source Code - 3.4 KB - MD5: 28071dfefba2ca9109976f6142dc7378 Tests for functions in "operators.py".
Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Oct 8, 2024 Mandl, Alexander; Bechtold, Marvin; Barzen, Johanna; Leymann, Frank, 2024, "Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"", https://doi.org/10.18419/DARUS-4113, DaRUS, V1 Replication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables. This is the version of the code that was used to generate the experiment results in the related publication. Experiments: - exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Sch...
cuda_qnn.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 24.6 KB - MD5: de37947e29cd064e381011cfc7b1f24d Collection of various QNN implementations. For the paper the implementation UnitaryParametrization is used.

plot_2.pdf

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Adobe PDF - 135.9 KB -

Plot from the generated data of experiment 1.

plot.ipynb

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Jupyter Notebook - 58.7 KB -

The main entry point to plot generated data. Requires the data as CSV file.

README.md

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Markdown Text - 2.1 KB -

Readme file with additional information on the data and code.

requirements.txt

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Plain Text - 2.1 KB -

Python requirements for reproduction.

states.py

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Python Source Code - 1.3 KB -

Functions to generate entangled states

test_cut.py

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Python Source Code - 2.7 KB -

Tests for functions in "cut.py".

test_mubs.py

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Python Source Code - 1.2 KB -

Tests for functions in "mubs.py".

test_operators.py

Dec 16, 2024 - Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"

Python Source Code - 3.4 KB -

Tests for functions in "operators.py".

Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Oct 8, 2024

Mandl, Alexander; Bechtold, Marvin; Barzen, Johanna; Leymann, Frank, 2024, "Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"", https://doi.org/10.18419/DARUS-4113, DaRUS, V1

Replication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables. This is the version of the code that was used to generate the experiment results in the related publication. Experiments: - exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Sch...

cuda_qnn.py

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

Python Source Code - 24.6 KB -

Collection of various QNN implementations. For the paper the implementation UnitaryParametrization is used.

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