1 to 10 of 22 Results
Python Source Code - 5.6 KB -
MD5: fb27515fd970f4ae24ec784a95135668
Functions to execute the experiment. |
Python Source Code - 2.0 KB -
MD5: 1fc9a5bf680067c551ab1383bf284237
Functions for generating non-maximally entangled states. |
Jupyter Notebook - 14.1 KB -
MD5: ae52057f8c26b99ebe6441530afee887
The main entry point to run new experiments. |
Jupyter Notebook - 41.7 KB -
MD5: 1e4cd09824803ddcc107348c88583166
The main entry point to plot generated data. Requires the data as CSV file. |
Python Source Code - 4.8 KB -
MD5: 894906f22dbd93ef4e3308dae176b06d
Functions to perform quantum teleportation. |
Python Source Code - 2.3 KB -
MD5: 036a4b11c59ddedcf7cd58d159b52f72
Utility functions for the experiments. |
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. |
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. |
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. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 273 B -
MD5: b1a0c53c5525b9980bb82bf3784e3c7a
Various functions to modify the loss function after evaluation. |