Quantum Computing @IAAS

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

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11 to 20 of 98 Results

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".
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.
data_extended.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 15.3 KB - MD5: 1a8b536beb4f0ff6160a56dbeac17078 Functions for generating training data of various structures.
data_extraction.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 9.8 KB - MD5: 03bb5294f00ed8f2a2cd92b5e22ddb8b Analyzes raw experiment data to compute means and standard deviation of various metrics.

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".

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.

data_extended.py

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

Python Source Code - 15.3 KB -

Functions for generating training data of various structures.

data_extraction.py

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

Python Source Code - 9.8 KB -

Analyzes raw experiment data to compute means and standard deviation of various metrics.

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