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File Type: Archive

1 to 8 of 8 Results

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)
calibration_data.zip 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.
cut_qaoa_code.zip 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.
experiment_complete.zip Jul 11, 2023 - Data repository for: Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices ZIP Archive - 5.5 GB - MD5: 529f04ad1ef749254caf13216615deeb This archive encompasses both the raw and partially processed data generated during the experiments. To analyze and evaluate the data, please utilize the code provided in the cut_qaoa_code.zip archive and refer to the instructions outlined in the accompanying readme file.
plots.zip Jul 11, 2023 - Data repository for: Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices ZIP Archive - 162 B - MD5: 234e0cb67627bd2439c33df41027bf72 This archive contains supplementary plots and visualizations. Within the archive, each graph is allocated its own dedicated folder that conveniently stores all the relevant plots associated with that specific graph.

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)

calibration_data.zip

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 -

This archive contains the calibration data of the quantum devices utilized during the time period when the experiments were conducted.

cut_qaoa_code.zip

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 -

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.

experiment_complete.zip

Jul 11, 2023 - Data repository for: Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

ZIP Archive - 5.5 GB -

This archive encompasses both the raw and partially processed data generated during the experiments. To analyze and evaluate the data, please utilize the code provided in the cut_qaoa_code.zip archive and refer to the instructions outlined in the accompanying readme file.

plots.zip

Jul 11, 2023 - Data repository for: Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

ZIP Archive - 162 B -

This archive contains supplementary plots and visualizations. Within the archive, each graph is allocated its own dedicated folder that conveniently stores all the relevant plots associated with that specific graph.

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