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41 to 50 of 98 Results
nme_basis.py
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Python Source Code - 2.0 KB - MD5: 1fc9a5bf680067c551ab1383bf284237
Functions for generating non-maximally entangled states.
numerical_simulation.ipynb
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Jupyter Notebook - 14.1 KB - MD5: ae52057f8c26b99ebe6441530afee887
The main entry point to run new experiments.
plots.ipynb
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Jupyter Notebook - 41.7 KB - MD5: 1e4cd09824803ddcc107348c88583166
The main entry point to plot generated data. Requires the data as CSV file.
README.md
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Markdown Text - 2.7 KB - MD5: 14c26b62957d6d0d54a3bb8c129ef11a
Readme file with additional information on the data and code.
requirements.txt
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Plain Text - 2.2 KB - MD5: f74ff9705432d88bf78f186c1d8a26ed
Python requirements for reproduction.
teleportation.py
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Python Source Code - 4.8 KB - MD5: 894906f22dbd93ef4e3308dae176b06d
Functions to perform quantum teleportation.
util.py
Jan 29, 2024 - Data repository for: Cutting a Wire with Non-Maximally Entangled States
Python Source Code - 2.3 KB - MD5: 036a4b11c59ddedcf7cd58d159b52f72
Utility functions for the experiments.
avg_rank_exp.py
Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 2.3 KB - MD5: 3092220f87583ca17e7cfd73fdacafef
Experiments for training QNNs using training data of varying Schmidt rank.
classic_training.py
Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 1.5 KB - MD5: ff77e11ba2a65fbf30afe3680e7903e6
Cost function and training routines procedures for PyTorch QNN simulation.
config.py
Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 3.8 KB - MD5: b0b7ff540bb8e1b631cff61a652b06f7
Configuration structures for experiments.
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