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Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 2.9 KB -
MD5: e03fd7afffe888e2895e000bc5d68279
Utility functions for visualisation. |
ZIP Archive - 732.9 MB -
MD5: 4b44f0e1d48bfd7ecd536d016b120dd1
Raw data (losses and risks) for training QNNs using entangled training data with varying degree of entanglement.
Directory t[a]r[b] contains results for QNNs trained with "a" training samples of average Schmidt rank "b".
For analyzed results see avg_rank_risks.npy. |
Adobe PDF - 70.2 KB -
MD5: 21850b0dc649f6572df2431daf6e5c6f
The average risk after training a 6-qubit QNN for randomly generated target unitaries. For this experiment, data of varying Schmidt rank with average ranks r ∈ {1,2,4,64} and different input sizes t ∈ {1,2,4,8,16,32,64} was used. The markers give the experimentally computed avera... |
Unknown - 1.0 KB -
MD5: 3ab5355e889b0bdc4038631e8198b157
Map (r: int-> (t: int -> l: float)) of average final losses "l" after training of QNNs with "t" entangled datasets of average Schmidt rank "r". |
Adobe PDF - 158.8 KB -
MD5: e2e6fbb55a2e0cb2f2fd0c46f3086cc4
The average losses at the end of training for QNNs using training data of varying Schmidt ranks with mean Schmidt ranks in {1,2,4,64}. |
Unknown - 618 B -
MD5: 64cd3be5713e8bbe896a1234fd657aef
Map (r: int-> (t: int -> R: float)) of average final risks "R" after training of QNNs with "t" entangled datasets of average Schmidt rank "r". |
Adobe PDF - 165.4 KB -
MD5: 7591bfc8ed33637d989ac57790ccf6a4
Figure 1: The eigenvalues introduced by U†VS after training with four orthogonal inputs for a for a low-risk hypothesis (left) and for a high-risk hypothesis (right). |
Adobe PDF - 68.6 KB -
MD5: 80bc1f5e3db61210162ddf4b8ffe23d3
The average risk after training a 6-qubit QNN for randomly generated target unitaries using linearly dependent data according. For each number of training pairs t, the Schmidt rank is chosen as such that r · t = d. The lower bound for the risk for this configuration is shown as a... |
Unknown - 184 B -
MD5: d938321dcf959b96639d9543ae6e759d
Array containing the average risks after training with t = 1,2,4,8,16,32,64 entangled training samples that are not linearly independent in H_X. |
ZIP Archive - 182.8 MB -
MD5: 8dba18ba2b5bd59caebd71d64e04c17f
Raw data (losses and risks) for training QNNs using entangled training data that is not linearly independent in H_X.
Directory t[a] contains results for QNNs trained with "a" training samples.
For analyzed results see nlihx_exp_points.npy. |