https://github.com/boothgroup/excitation_amplitude_sampling
Code for the Excitation Amplitude Sampling with Classical shadows paper
Science Score: 26.0%
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Low similarity (5.6%) to scientific vocabulary
Last synced: 10 months ago
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Code for the Excitation Amplitude Sampling with Classical shadows paper
Basic Info
- Host: GitHub
- Owner: BoothGroup
- Language: Jupyter Notebook
- Default Branch: main
- Size: 12.4 MB
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Created about 1 year ago
· Last pushed about 1 year ago
https://github.com/BoothGroup/excitation_amplitude_sampling/blob/main/
# Data for excitation amplitude sampling paper Classical shadows and quantum cluster solvers The directory is structured as follows: - src contains the core code for using classical shadows (classical_shadow.py) and quantum cluster solvers for embedding (cluster_solver.py) - data_collection_scripts contains the code which was run to collect the data used in the (upcoming) publication. - hydrogen_results_comp_fci.py is for the figure coparing the different approaches of measuring the energy given a quantum circuit - in this case with an FCI wavefcuntion as a stand in - hydrogen_results_comp_dmrg.py is the same but using a dmrg wavefunction as a stand in - noise_models_hydrogen_circuits.py does the interpolation of the amount of noise and compares the result of using the expectation value and mixed estimator using a quantum simulator - the scripts in the real devices folder contain the code to create circuits for the gound sate using ffsim and store in a pickle file and then run on an IBM backend - H_properties_comp_all_ci.py contains the code to calculate the RDMs by mapping the ci amplitudes to a CCSD ansatz - the callback_BN/NiO.py and NiO_fci_embedding_tests.py contain examples of using the cluster_solver code to do wavefunction embedding - plots/data conatins the data and plotting scripts to recreate the plots in the publication - Figure 1 a/b are done using fci_energy_method_comp_plots.ipynb - Figure 2 using noise_model_plotting.ipynb - Figure 3 a/b using szsz_plotting.ipynb - Figure 4 & 5 use BN_plotting.ipynb and callback_NiO_plotting.ipynb respectively
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- Name: BoothGroup
- Login: BoothGroup
- Kind: organization
- Repositories: 6
- Profile: https://github.com/BoothGroup
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