71 to 80 of 98 Results
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 dashed line, the expected risk according to the proposed new lower bo... |
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. |
Adobe PDF - 68.7 KB -
MD5: 60f2614c7fffce46d724ccc90c2e1a10
The average risk after training a 6-qubit QNN for randomly generated target unitaries using orthogonal data. 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 dashed line. The expected risk according to the proposed new lower bound is shown as a... |
Unknown - 184 B -
MD5: 7d96b191470e360b62deb994ecdf808c
Array containing the average risks after training with t = 1,2,4,8,16,32,64 orthogonal entangled training samples. |
ZIP Archive - 183.6 MB -
MD5: 808a025fe107f06ba43821ed90fd3036
Raw data (losses and risks) for training QNNs using orthogonal entangled training data.
Directory t[a] contains results for QNNs trained with "a" training samples.
For analyzed results see orthogonal_exp_points.npy |
ZIP Archive - 7.3 KB -
MD5: 6068205158d0b55c221d3dffec6af2b6
Contains training data (X), the target unitary (U) and the resulting unitary (V) as Numpy-Files for training using four orthogonal training samples for a high risk QNN and a low risk QNN (see Figure 1) |
Jul 11, 2023 -
Data repository for: Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
ZIP Archive - 4.3 MB -
MD5: 536f99a9c5510b62642a9bb2b7ff6e07
This archive contains the calibration data of the quantum devices utilized during the time period when the experiments were conducted. |
Jul 11, 2023 -
Data repository for: Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
ZIP Archive - 56.8 MB -
MD5: defed79127a6c2c6d1619ba9884ceeb5
This archive contains the Python code used to conduct the experiments and evaluate their results. To execute the code, please refer to the instructions provided in the readme file located within the archive. |
