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Installation

  1. Create a virtual environment, e.g. using conda create --name qugen_env python=3.9.12. Python 3.9 or later is supported.
  2. Activate the enviroment, e.g source activate qugen_env.
  3. Run pip install . or pip install -e . to install it in editable mode.

Instructions for training and running models

Each model type has an example script to create and train a model found at ''apps/logistics/train_xyz.py'' file, e.g. ''apps/logistics/train_continuous_qgan.py''

The training data, data transformation and other hyperparameters can all be specified in these training scripts.

Each model type also has a model handler, they are found in qugen/main/generator and are imported by each training script as needed.

The model handlers contain four important methods, shown in the table below.

Method Description
build Builds a model by specifying its hyperparameters.
train Trains a model using a training data set.
predict Samples from a trained model.
reload Reloads a trained model from file.

To train a model, the training scripts work as follows:

  • Each train file loads an input training data set from file (found in training_data/) and creates the appropriate model handler.
  • The model handler builds a model from the specified parameters: model_name, data_set_name, n_qubits, n_registers, circuit_depth, n_epochs etc.
  • A meta json file of the model is saved to experiments/modelname.
  • The built model is then trained using the training data set.
  • During training, at each epoch, the weights and a log file (containing the KL divergence) are saved to experiments/modelname.

To load a model from file (see train_discrete_copula_qcbm for example):

  • Create an instance of the appropriate model handler (e.g. model = ContinuousQGANModelHandler()).
  • Load the model using the reload functions and the meta json and your chosen parameters file.

To sample from a trained model (see train_discrete_copula_qcbm for example):

  • Use the model.predict method, which takes as an argument the number of samples to be generated.

To evaluate a trained model:

  • Use the evaluator method which takes all the weights from the training run of the model and finds the set of weights with the lowest cost function (KL divergence)

Citation

@article{riofrio2023performance,
  title={A performance characterization of quantum generative models},
  author={Riofr{\'\i}o, Carlos A and Mitevski, Oliver and Jones, Caitlin and Krellner, Florian and Vu{\v{c}}kovi{\'c}, Aleksandar and Doetsch, Joseph and Klepsch, Johannes and Ehmer, Thomas and Luckow, Andre},
  journal={arXiv preprint arXiv:2301.09363},
  year={2023}
}

Contact

[email protected]