Institute for Architecture for Application Systems

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31 to 40 of 2,454 Results

exp_setup.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 494 B - MD5: 2f2da4b030d7a0517332fd9cb80a2ad4 General setup code for experiments - is imported in the experiment entry points.
plot_coeffvariation.pdf Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Adobe PDF - 325.3 KB - MD5: 36fb8c03d1f896bb593bd4886bcf2bfd Figure 1: Average risk of QNNs that are trained with training samples of varying Schmidt rank r and varying coefficient c_1 for the Schmidt basis state \|γ⟩ = U^†\|o⟩.
plot_obs_risks.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 4.8 KB - MD5: 2faa5357c3da420669cf42ad353e24d7 Main entry points for plotting figures from the paper.
plot_random.pdf Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Adobe PDF - 201.6 KB - MD5: 11f669c77e88e3114732fec1b66cac8c Figure 2: Average risk of QNNs that are trained with training samples of varying Schmidt rank r comprised of randomly sampled Schmidt basis vectors.
pt_train.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 7.9 KB - MD5: 7cf85753480f2bb138f59e5d96735313 The QNN training routines that are used in the experiments.
qnn.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 567 B - MD5: 2d59023ea07e02020019970d499218fa Structure of QNN implementations.
quantum_gates.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 4.4 KB - MD5: 880496a1590c56ea84ba76c2c826db7a Collection of various quantum gates implemented using pytorch for the QNN implementations.
requirements.txt Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Plain Text - 113 B - MD5: 2afe47d43d99e1cebff654aec793343a Python requirements for experiments.
risk.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 3.5 KB - MD5: 451c38f7f182216209d265037c71722a Utility functions for computing risk.
sampling_error_extension.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 9.8 KB - MD5: fca958b2e95c257b8cebd29fc81b41fd Functions/Classes required for training with observables/training by sampling from measurements.

exp_setup.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 494 B -

General setup code for experiments - is imported in the experiment entry points.

plot_coeffvariation.pdf

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Adobe PDF - 325.3 KB -

Figure 1: Average risk of QNNs that are trained with training samples of varying Schmidt rank r and varying coefficient c_1 for the Schmidt basis state |γ⟩ = U^†|o⟩.

plot_obs_risks.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 4.8 KB -

Main entry points for plotting figures from the paper.

plot_random.pdf

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Adobe PDF - 201.6 KB -

Figure 2: Average risk of QNNs that are trained with training samples of varying Schmidt rank r comprised of randomly sampled Schmidt basis vectors.

pt_train.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 7.9 KB -

The QNN training routines that are used in the experiments.

qnn.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 567 B -

Structure of QNN implementations.

quantum_gates.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 4.4 KB -

Collection of various quantum gates implemented using pytorch for the QNN implementations.

requirements.txt

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Plain Text - 113 B -

Python requirements for experiments.

risk.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 3.5 KB -

Utility functions for computing risk.

sampling_error_extension.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 9.8 KB -

Functions/Classes required for training with observables/training by sampling from measurements.

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