Do this first. See the ENV_SETUP documentation.
Do this second :-)
See the documentation in the data directory.
If you have set up the environment and data, then move to the data analysis notebook (00_), then the model training (01_), etc.
To open your JupyterLab and then the notebooks:
conda activate qubit-design-env
jupyter-lab
To back up models that are to large for github, I use the drive here. Please email [email protected] if you can't access it for some reason.
The following three folders contain scripts to use Machine Learning to predict Qiskit design parameters based on target hamiltonian values for each specified design part:
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model_predict_cavity_claw_RouteMeander_eigenmode
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model_predict_coupler_NCap_cap_matrix
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model_predict_qubit_TransmonCross_cap_matrix
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ml_00_data_analysis:loads the data and parses it into a format to use in the next script
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ml_01_train_keras: Trains the model using an MLP
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ml_hyperparameter_search_analysis: provides plots to visualize the hyperparameter search, so the user can easily see the best values
- Three models have been trained with optimized hyperparameters from keras-tuner, each model predicting Qiskit metal parameters for various parts of a transmon cross chip/resonator design
- Different encoding values were tested and optimized for the categorical y parameters in Qiskit Metal
- Different scaling techniques were implemented, where both X and Y are scaled
- Training and validation sets were explicitly seperated
Below is the desired flow for this project. All three models will be stitched together to predict a Qiskit Metal design when given a set of desired "Top_Level_X" Hamiltonian values. This will be done using the "X_2.0" values that are simulated from the 'y' values predicted with each of the 3 individual models:
