1 to 10 of 30 Results
Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/darus-3442, DaRUS, V1
Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets. The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the re... |
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. |
