Institute for Architecture for Application Systems

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File Type: Archive License: CC BY 4.0

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average_rank_results.zip Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs 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.
not_linearly_independent_results.zip Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs 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.
orthogonal_results.zip Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs 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
phases_in_orthogonal_training.zip Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs 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)

average_rank_results.zip

Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

ZIP Archive - 732.9 MB -

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.

not_linearly_independent_results.zip

Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

ZIP Archive - 182.8 MB -

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.

orthogonal_results.zip

Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

ZIP Archive - 183.6 MB -

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

phases_in_orthogonal_training.zip

Sep 27, 2023 - Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs

ZIP Archive - 7.3 KB -

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)

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